Impact of Interstock and Rootstock on the Growth and Productivity of Mango (Mangifera indica L.) in the San Lorenzo Valley, Peru

1 Project Setup

library(emmeans)
library(corrplot)
library(multcomp)
library(FSA)
library(factoextra)
library(corrplot)
library(magrittr)
source('https://inkaverse.com/setup.r')

Project directory:  C:/INIA/GIT/prochira_injertos 
CPU cores detected:  28 
CPU cores in use:  22 

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11 ucrt)
 os       Windows 11 x64 (build 26100)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Peru.utf8
 ctype    Spanish_Peru.utf8
 tz       America/Lima
 date     2025-10-22
 pandoc   3.6.3 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
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 agricolae       1.3-7   2023-10-22 [1] CRAN (R 4.5.0)
 AlgDesign       1.2.1.2 2025-04-06 [1] CRAN (R 4.5.0)
 askpass         1.2.1   2024-10-04 [1] CRAN (R 4.5.0)
 boot            1.3-31  2024-08-28 [2] CRAN (R 4.5.0)
 cachem          1.1.0   2024-05-16 [1] CRAN (R 4.5.0)
 car           * 3.1-3   2024-09-27 [1] CRAN (R 4.5.0)
 carData       * 3.0-5   2022-01-06 [1] CRAN (R 4.5.0)
 cellranger      1.1.0   2016-07-27 [1] CRAN (R 4.5.0)
 cli             3.6.5   2025-04-23 [1] CRAN (R 4.5.0)
 cluster         2.1.8.1 2025-03-12 [2] CRAN (R 4.5.0)
 codetools       0.2-20  2024-03-31 [2] CRAN (R 4.5.0)
 corrplot      * 0.95    2024-10-14 [1] CRAN (R 4.5.0)
 cowplot       * 1.1.3   2024-01-22 [1] CRAN (R 4.5.0)
 curl            6.2.2   2025-03-24 [1] CRAN (R 4.5.0)
 devtools      * 2.4.5   2022-10-11 [1] CRAN (R 4.5.0)
 digest          0.6.37  2024-08-19 [1] CRAN (R 4.5.0)
 dplyr         * 1.1.4   2023-11-17 [1] CRAN (R 4.5.0)
 DT              0.33    2024-04-04 [1] CRAN (R 4.5.0)
 ellipsis        0.3.2   2021-04-29 [1] CRAN (R 4.5.0)
 emmeans       * 1.11.1  2025-05-04 [1] CRAN (R 4.5.0)
 estimability    1.5.1   2024-05-12 [1] CRAN (R 4.5.0)
 evaluate        1.0.3   2025-01-10 [1] CRAN (R 4.5.0)
 factoextra    * 1.0.7   2020-04-01 [1] CRAN (R 4.5.0)
 FactoMineR    * 2.11    2024-04-20 [1] CRAN (R 4.5.0)
 farver          2.1.2   2024-05-13 [1] CRAN (R 4.5.0)
 fastmap         1.2.0   2024-05-15 [1] CRAN (R 4.5.0)
 flashClust      1.01-2  2012-08-21 [1] CRAN (R 4.5.0)
 forcats       * 1.0.0   2023-01-29 [1] CRAN (R 4.5.0)
 Formula         1.2-5   2023-02-24 [1] CRAN (R 4.5.0)
 fs              1.6.6   2025-04-12 [1] CRAN (R 4.5.0)
 FSA           * 0.10.0  2025-05-06 [1] CRAN (R 4.5.0)
 gargle          1.5.2   2023-07-20 [1] CRAN (R 4.5.0)
 generics        0.1.4   2025-05-09 [1] CRAN (R 4.5.0)
 ggplot2       * 3.5.2   2025-04-09 [1] CRAN (R 4.5.0)
 ggrepel         0.9.6   2024-09-07 [1] CRAN (R 4.5.0)
 glue            1.8.0   2024-09-30 [1] CRAN (R 4.5.0)
 googledrive   * 2.1.1   2023-06-11 [1] CRAN (R 4.5.0)
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 gtable          0.3.6   2024-10-25 [1] CRAN (R 4.5.0)
 hms             1.1.3   2023-03-21 [1] CRAN (R 4.5.0)
 htmltools       0.5.8.1 2024-04-04 [1] CRAN (R 4.5.0)
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 httpuv          1.6.16  2025-04-16 [1] CRAN (R 4.5.0)
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 Rcpp            1.0.14  2025-01-12 [1] CRAN (R 4.5.0)
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 [1] C:/Users/INIA/AppData/Local/R/win-library/4.5
 [2] C:/Program Files/R/R-4.5.0/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

cat("Project: ", getwd(), "\n")

Project:  C:/INIA/GIT/prochira_injertos 

session_info()

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11 ucrt)
 os       Windows 11 x64 (build 26100)
 system   x86_64, mingw32
 ui       RTerm
 language (EN)
 collate  Spanish_Peru.utf8
 ctype    Spanish_Peru.utf8
 tz       America/Lima
 date     2025-10-22
 pandoc   3.6.3 @ C:/Program Files/RStudio/resources/app/bin/quarto/bin/tools/ (via rmarkdown)
 quarto   NA @ C:\\PROGRA~1\\RStudio\\RESOUR~1\\app\\bin\\quarto\\bin\\quarto.exe

─ Packages ───────────────────────────────────────────────────────────────────
 package       * version date (UTC) lib source
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 agricolae       1.3-7   2023-10-22 [1] CRAN (R 4.5.0)
 AlgDesign       1.2.1.2 2025-04-06 [1] CRAN (R 4.5.0)
 askpass         1.2.1   2024-10-04 [1] CRAN (R 4.5.0)
 boot            1.3-31  2024-08-28 [2] CRAN (R 4.5.0)
 cachem          1.1.0   2024-05-16 [1] CRAN (R 4.5.0)
 car           * 3.1-3   2024-09-27 [1] CRAN (R 4.5.0)
 carData       * 3.0-5   2022-01-06 [1] CRAN (R 4.5.0)
 cellranger      1.1.0   2016-07-27 [1] CRAN (R 4.5.0)
 cli             3.6.5   2025-04-23 [1] CRAN (R 4.5.0)
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 curl            6.2.2   2025-03-24 [1] CRAN (R 4.5.0)
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 ellipsis        0.3.2   2021-04-29 [1] CRAN (R 4.5.0)
 emmeans       * 1.11.1  2025-05-04 [1] CRAN (R 4.5.0)
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 [1] C:/Users/INIA/AppData/Local/R/win-library/4.5
 [2] C:/Program Files/R/R-4.5.0/library
 * ── Packages attached to the search path.

──────────────────────────────────────────────────────────────────────────────

2 Import data

Data were imported from the field book evaluated during the 2017-2019 growing seasons. The evaluations focused on the agronomic traits and fruit biometrics of the mango crop.

url <- "https://docs.google.com/spreadsheets/d/1E_cUmiRFoPLGAj7AQC6HZBD6hXtqzg2UHPvtWr2QR_U/edit#gid=2112492836"

gs <- url %>% 
  as_sheets_id()

ley <- gs %>% 
  range_read("leyenda") %>% 
  rename(tratamientos = TRATAM) %>% 
  rename_with(~ tolower(.))

rdt <- gs %>% 
  range_read("db") %>% 
  merge(., ley) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , height = alt_planta
         , n_fruits = n_frutos
         , flowering = per_floracion
         , sproud = per_brote
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(across(year:n_plant, ~ as.factor(.))) 

glimpse(rdt)
## Rows: 648
## Columns: 13
## $ year      <fct> 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, …
## $ n         <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 1…
## $ treat     <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 231, 231, 231, …
## $ n_treat   <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 8, 8, …
## $ stock     <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ edge      <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, …
## $ scion     <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, …
## $ block     <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
## $ n_plant   <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, 3, 4, 5, 6, 7, 8, 9, 1, 2, …
## $ height    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
## $ n_fruits  <dbl> 170, 200, 310, NA, 235, 185, 180, 132, 80, 231, 198, 195, 20…
## $ flowering <dbl> 60, 50, 50, NA, 80, 60, 40, 90, 90, 60, 70, 40, 90, 80, 80, …
## $ sproud    <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, …
  
fru <-  gs %>% 
  range_read("db_frutos") %>% 
  merge(., ley, ) %>% 
  dplyr::select(year = "año", n, tratamientos,n_trat:yema, everything()) %>% 
  rename(treat = tratamientos
         , n_treat = n_trat
         , block = bloque
         , n_plant = n_planta
         , weigth = peso
         , long = largo
         , n_fruits = n_frutos
         , diameter_1 = diametro_1
         , diameter_2 = diametro_2
         , sample = muestra
         , scion = yema
         , stock = patron
         , edge = puente
         ) %>% 
  dplyr::arrange(year, n, treat) %>% 
  mutate(diameter_average = (diameter_1 + diameter_2)/2) %>% 
  mutate(across(year:sample, ~ as.factor(.)))

glimpse(fru)
## Rows: 240
## Columns: 16
## $ year             <fct> 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023, 2023,…
## $ n                <fct> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16…
## $ treat            <fct> 141, 141, 141, 141, 141, 141, 141, 141, 141, 141, 231…
## $ n_treat          <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2,…
## $ stock            <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ edge             <fct> CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULUCANAS, CHULU…
## $ scion            <fct> KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT, KENT,…
## $ block            <fct> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
## $ n_plant          <fct> 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 1, 1, 1, 1, 1, 2, 2, 2,…
## $ sample           <fct> 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3, 4, 5, 1, 2, 3,…
## $ n_fruits         <dbl> 26, 26, 26, 26, 26, 35, 35, 35, 35, 35, 118, 118, 118…
## $ weigth           <dbl> 270, 520, 385, 230, 285, 600, 457, 520, 665, 305, 422…
## $ long             <dbl> 86, 111, 103, 86, 88, 116, 101, 115, 126, 91, 100, 10…
## $ diameter_1       <dbl> 78, 90, 86, 70, 78, 96, 88, 88, 103, 78, 91, 84, 84, …
## $ diameter_2       <dbl> 74, 83, 75, 69, 71, 87, 88, 81, 95, 80, 83, 76, 72, 8…
## $ diameter_average <dbl> 76.0, 86.5, 80.5, 69.5, 74.5, 91.5, 88.0, 84.5, 99.0,…

ley %>% kable(caption = "Interstock grafting treatments", align = 'c')

Interstock grafting treatments

n_trat	tratamientos	patron	puente	yema
1	141	CHULUCANAS	CHULUCANAS	KENT
2	231	CHATO	JULIE	KENT
3	241	CHULUCANAS	CHATO	KENT
4	211	CHATO	IRWIN	KENT
5	131	CHULUCANAS	JULIE	KENT
6	111	CHULUCANAS	IRWIN	KENT
7	221	CHATO	CHATO	KENT
8	121	CHATO	CHULUCANAS	KENT

rdt %>% kable(caption = "Evaluation of the agronomic characteristics of mango", align = 'c')

Evaluation of the agronomic characteristics of mango

year	n	treat	n_treat	stock	edge	scion	block	n_plant	height	n_fruits	flowering	sproud
2017	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1		170	60
2017	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2		200	50
2017	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	3		310	50
2017	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	4
2017	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	5		235	80
2017	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	6		185	60
2017	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	7		180	40
2017	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	8		132	90
2017	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	9		80	90
2017	10	231	2	CHATO	JULIE	KENT	1	1		231	60
2017	11	231	2	CHATO	JULIE	KENT	1	2		198	70
2017	12	231	2	CHATO	JULIE	KENT	1	3		195	40
2017	13	231	2	CHATO	JULIE	KENT	1	4		200	90
2017	14	231	2	CHATO	JULIE	KENT	1	5		180	80
2017	15	231	2	CHATO	JULIE	KENT	1	6		140	80
2017	16	231	2	CHATO	JULIE	KENT	1	7		200	60
2017	17	231	2	CHATO	JULIE	KENT	1	8		186	75
2017	18	231	2	CHATO	JULIE	KENT	1	9
2017	19	121	8	CHATO	CHULUCANAS	KENT	1	1
2017	20	121	8	CHATO	CHULUCANAS	KENT	1	2		165	75
2017	21	121	8	CHATO	CHULUCANAS	KENT	1	3		210	75
2017	22	121	8	CHATO	CHULUCANAS	KENT	1	4		201	80
2017	23	121	8	CHATO	CHULUCANAS	KENT	1	5		110	80
2017	24	121	8	CHATO	CHULUCANAS	KENT	1	6		215	90
2017	25	121	8	CHATO	CHULUCANAS	KENT	1	7		200	80
2017	26	121	8	CHATO	CHULUCANAS	KENT	1	8		160	80
2017	27	121	8	CHATO	CHULUCANAS	KENT	1	9		162	75
2017	28	211	4	CHATO	IRWIN	KENT	1	1		204	70
2017	29	211	4	CHATO	IRWIN	KENT	1	2		124	80
2017	30	211	4	CHATO	IRWIN	KENT	1	3		130	80
2017	31	211	4	CHATO	IRWIN	KENT	1	4		6	5
2017	32	211	4	CHATO	IRWIN	KENT	1	5		90	35
2017	33	211	4	CHATO	IRWIN	KENT	1	6		170	60
2017	34	211	4	CHATO	IRWIN	KENT	1	7		165	45
2017	35	211	4	CHATO	IRWIN	KENT	1	8		100	25
2017	36	211	4	CHATO	IRWIN	KENT	1	9		70	25
2017	37	131	5	CHULUCANAS	JULIE	KENT	1	1		75	80
2017	38	131	5	CHULUCANAS	JULIE	KENT	1	2		200	80
2017	39	131	5	CHULUCANAS	JULIE	KENT	1	3		184	80
2017	40	131	5	CHULUCANAS	JULIE	KENT	1	4		102	70
2017	41	131	5	CHULUCANAS	JULIE	KENT	1	5		190	60
2017	42	131	5	CHULUCANAS	JULIE	KENT	1	6		180	70
2017	43	131	5	CHULUCANAS	JULIE	KENT	1	7		175	75
2017	44	131	5	CHULUCANAS	JULIE	KENT	1	8		120	80
2017	45	131	5	CHULUCANAS	JULIE	KENT	1	9		155	60
2017	46	241	3	CHULUCANAS	CHATO	KENT	1	1		230	90
2017	47	241	3	CHULUCANAS	CHATO	KENT	1	2		110	90
2017	48	241	3	CHULUCANAS	CHATO	KENT	1	3		185	90
2017	49	241	3	CHULUCANAS	CHATO	KENT	1	4		220	90
2017	50	241	3	CHULUCANAS	CHATO	KENT	1	5		180	90
2017	51	241	3	CHULUCANAS	CHATO	KENT	1	6		175	60
2017	52	241	3	CHULUCANAS	CHATO	KENT	1	7		210	90
2017	53	241	3	CHULUCANAS	CHATO	KENT	1	8		180	90
2017	54	241	3	CHULUCANAS	CHATO	KENT	1	9		178	90
2017	55	111	6	CHULUCANAS	IRWIN	KENT	1	1		150	90
2017	56	111	6	CHULUCANAS	IRWIN	KENT	1	2		160	85
2017	57	111	6	CHULUCANAS	IRWIN	KENT	1	3		200	90
2017	58	111	6	CHULUCANAS	IRWIN	KENT	1	4		140	90
2017	59	111	6	CHULUCANAS	IRWIN	KENT	1	5		175	90
2017	60	111	6	CHULUCANAS	IRWIN	KENT	1	6		200	80
2017	61	111	6	CHULUCANAS	IRWIN	KENT	1	7		208	90
2017	62	111	6	CHULUCANAS	IRWIN	KENT	1	8		300	90
2017	63	111	6	CHULUCANAS	IRWIN	KENT	1	9		230	90
2017	64	221	7	CHATO	CHATO	KENT	1	1		190	90
2017	65	221	7	CHATO	CHATO	KENT	1	2		206	90
2017	66	221	7	CHATO	CHATO	KENT	1	3		145	80
2017	67	221	7	CHATO	CHATO	KENT	1	4		51	20
2017	68	221	7	CHATO	CHATO	KENT	1	5		45	30
2017	69	221	7	CHATO	CHATO	KENT	1	6		163	80
2017	70	221	7	CHATO	CHATO	KENT	1	7		35	15
2017	71	221	7	CHATO	CHATO	KENT	1	8		200	80
2017	72	221	7	CHATO	CHATO	KENT	1	9		32	15
2017	73	211	4	CHATO	IRWIN	KENT	2	1		170	90
2017	74	211	4	CHATO	IRWIN	KENT	2	2		350	80
2017	75	211	4	CHATO	IRWIN	KENT	2	3		200	90
2017	76	211	4	CHATO	IRWIN	KENT	2	4		154	70
2017	77	211	4	CHATO	IRWIN	KENT	2	5		180	90
2017	78	211	4	CHATO	IRWIN	KENT	2	6		130	90
2017	79	211	4	CHATO	IRWIN	KENT	2	7		145	90
2017	80	211	4	CHATO	IRWIN	KENT	2	8		130	90
2017	81	211	4	CHATO	IRWIN	KENT	2	9		220	90
2017	82	121	8	CHATO	CHULUCANAS	KENT	2	1		223	90
2017	83	121	8	CHATO	CHULUCANAS	KENT	2	2		220	90
2017	84	121	8	CHATO	CHULUCANAS	KENT	2	3		230	90
2017	85	121	8	CHATO	CHULUCANAS	KENT	2	4		200	90
2017	86	121	8	CHATO	CHULUCANAS	KENT	2	5		210	90
2017	87	121	8	CHATO	CHULUCANAS	KENT	2	6		150	90
2017	88	121	8	CHATO	CHULUCANAS	KENT	2	7		220	80
2017	89	121	8	CHATO	CHULUCANAS	KENT	2	8		270	90
2017	90	121	8	CHATO	CHULUCANAS	KENT	2	9		170	85
2017	91	231	2	CHATO	JULIE	KENT	2	1		28	90
2017	92	231	2	CHATO	JULIE	KENT	2	2		250	90
2017	93	231	2	CHATO	JULIE	KENT	2	3		115	70
2017	94	231	2	CHATO	JULIE	KENT	2	4		160	80
2017	95	231	2	CHATO	JULIE	KENT	2	5		200	90
2017	96	231	2	CHATO	JULIE	KENT	2	6		150	90
2017	97	231	2	CHATO	JULIE	KENT	2	7		120	90
2017	98	231	2	CHATO	JULIE	KENT	2	8		230	90
2017	99	231	2	CHATO	JULIE	KENT	2	9		240	80
2017	100	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1		210	80
2017	101	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2		205	90
2017	102	141	1	CHULUCANAS	CHULUCANAS	KENT	2	3		210	90
2017	103	141	1	CHULUCANAS	CHULUCANAS	KENT	2	4		170	80
2017	104	141	1	CHULUCANAS	CHULUCANAS	KENT	2	5		120	80
2017	105	141	1	CHULUCANAS	CHULUCANAS	KENT	2	6		240	90
2017	106	141	1	CHULUCANAS	CHULUCANAS	KENT	2	7		280	90
2017	107	141	1	CHULUCANAS	CHULUCANAS	KENT	2	8		260	90
2017	108	141	1	CHULUCANAS	CHULUCANAS	KENT	2	9		170	70
2017	109	221	7	CHATO	CHATO	KENT	2	1		142	90
2017	110	221	7	CHATO	CHATO	KENT	2	2		120	90
2017	111	221	7	CHATO	CHATO	KENT	2	3		140	90
2017	112	221	7	CHATO	CHATO	KENT	2	4		180	90
2017	113	221	7	CHATO	CHATO	KENT	2	5		160	90
2017	114	221	7	CHATO	CHATO	KENT	2	6		130	90
2017	115	221	7	CHATO	CHATO	KENT	2	7		70	90
2017	116	221	7	CHATO	CHATO	KENT	2	8		115	90
2017	117	221	7	CHATO	CHATO	KENT	2	9		104	90
2017	118	111	6	CHULUCANAS	IRWIN	KENT	2	1
2017	119	111	6	CHULUCANAS	IRWIN	KENT	2	2		208	90
2017	120	111	6	CHULUCANAS	IRWIN	KENT	2	3		110	90
2017	121	111	6	CHULUCANAS	IRWIN	KENT	2	4		140	90
2017	122	111	6	CHULUCANAS	IRWIN	KENT	2	5		108	80
2017	123	111	6	CHULUCANAS	IRWIN	KENT	2	6		120	90
2017	124	111	6	CHULUCANAS	IRWIN	KENT	2	7		250	90
2017	125	111	6	CHULUCANAS	IRWIN	KENT	2	8		220	90
2017	126	111	6	CHULUCANAS	IRWIN	KENT	2	9		235	80
2017	127	241	3	CHULUCANAS	CHATO	KENT	2	1		170	80
2017	128	241	3	CHULUCANAS	CHATO	KENT	2	2		182	80
2017	129	241	3	CHULUCANAS	CHATO	KENT	2	3		148	90
2017	130	241	3	CHULUCANAS	CHATO	KENT	2	4		160	90
2017	131	241	3	CHULUCANAS	CHATO	KENT	2	5		23	25
2017	132	241	3	CHULUCANAS	CHATO	KENT	2	6		175	80
2017	133	241	3	CHULUCANAS	CHATO	KENT	2	7		227	90
2017	134	241	3	CHULUCANAS	CHATO	KENT	2	8		180	90
2017	135	241	3	CHULUCANAS	CHATO	KENT	2	9		50	90
2017	136	131	5	CHULUCANAS	JULIE	KENT	2	1		180	80
2017	137	131	5	CHULUCANAS	JULIE	KENT	2	2		240	90
2017	138	131	5	CHULUCANAS	JULIE	KENT	2	3		124	70
2017	139	131	5	CHULUCANAS	JULIE	KENT	2	4		220	85
2017	140	131	5	CHULUCANAS	JULIE	KENT	2	5		240	80
2017	141	131	5	CHULUCANAS	JULIE	KENT	2	6		90	80
2017	142	131	5	CHULUCANAS	JULIE	KENT	2	7		220	85
2017	143	131	5	CHULUCANAS	JULIE	KENT	2	8		250	90
2017	144	131	5	CHULUCANAS	JULIE	KENT	2	9		174	90
2017	145	121	8	CHATO	CHULUCANAS	KENT	3	1		170	85
2017	146	121	8	CHATO	CHULUCANAS	KENT	3	2		198	80
2017	147	121	8	CHATO	CHULUCANAS	KENT	3	3		125	90
2017	148	121	8	CHATO	CHULUCANAS	KENT	3	4		124	90
2017	149	121	8	CHATO	CHULUCANAS	KENT	3	5		120	90
2017	150	121	8	CHATO	CHULUCANAS	KENT	3	6		135	80
2017	151	121	8	CHATO	CHULUCANAS	KENT	3	7		150	90
2017	152	121	8	CHATO	CHULUCANAS	KENT	3	8		330	90
2017	153	121	8	CHATO	CHULUCANAS	KENT	3	9		201	80
2017	154	211	4	CHATO	IRWIN	KENT	3	1		210	90
2017	155	211	4	CHATO	IRWIN	KENT	3	2		100	90
2017	156	211	4	CHATO	IRWIN	KENT	3	3		160	90
2017	157	211	4	CHATO	IRWIN	KENT	3	4		260	90
2017	158	211	4	CHATO	IRWIN	KENT	3	5
2017	159	211	4	CHATO	IRWIN	KENT	3	6		160	85
2017	160	211	4	CHATO	IRWIN	KENT	3	7		210	80
2017	161	211	4	CHATO	IRWIN	KENT	3	8		200	85
2017	162	211	4	CHATO	IRWIN	KENT	3	9		225	90
2017	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1		300	90
2017	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2		195	70
2017	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	3		210	80
2017	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	4		230	80
2017	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	5		205	90
2017	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	6		204	80
2017	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	7		90	90
2017	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	8		180	70
2017	171	141	1	CHULUCANAS	CHULUCANAS	KENT	3	9		37	20
2017	172	231	2	CHATO	JULIE	KENT	3	1		305	90
2017	173	231	2	CHATO	JULIE	KENT	3	2		120	90
2017	174	231	2	CHATO	JULIE	KENT	3	3		160	80
2017	175	231	2	CHATO	JULIE	KENT	3	4		82	90
2017	176	231	2	CHATO	JULIE	KENT	3	5		110	90
2017	177	231	2	CHATO	JULIE	KENT	3	6		140	80
2017	178	231	2	CHATO	JULIE	KENT	3	7		208	85
2017	179	231	2	CHATO	JULIE	KENT	3	8		240	90
2017	180	231	2	CHATO	JULIE	KENT	3	9		160	90
2017	181	111	6	CHULUCANAS	IRWIN	KENT	3	1		60	40
2017	182	111	6	CHULUCANAS	IRWIN	KENT	3	2		113	50
2017	183	111	6	CHULUCANAS	IRWIN	KENT	3	3		210	80
2017	184	111	6	CHULUCANAS	IRWIN	KENT	3	4		260	90
2017	185	111	6	CHULUCANAS	IRWIN	KENT	3	5		150	80
2017	186	111	6	CHULUCANAS	IRWIN	KENT	3	6		80	50
2017	187	111	6	CHULUCANAS	IRWIN	KENT	3	7		180	90
2017	188	111	6	CHULUCANAS	IRWIN	KENT	3	8		196	80
2017	189	111	6	CHULUCANAS	IRWIN	KENT	3	9		220	80
2017	190	221	7	CHATO	CHATO	KENT	3	1		230	90
2017	191	221	7	CHATO	CHATO	KENT	3	2		210	90
2017	192	221	7	CHATO	CHATO	KENT	3	3		150	85
2017	193	221	7	CHATO	CHATO	KENT	3	4		315	90
2017	194	221	7	CHATO	CHATO	KENT	3	5		220	90
2017	195	221	7	CHATO	CHATO	KENT	3	6
2017	196	221	7	CHATO	CHATO	KENT	3	7		250	90
2017	197	221	7	CHATO	CHATO	KENT	3	8		160	80
2017	198	221	7	CHATO	CHATO	KENT	3	9		230	90
2017	199	131	5	CHULUCANAS	JULIE	KENT	3	1
2017	200	131	5	CHULUCANAS	JULIE	KENT	3	2		280	90
2017	201	131	5	CHULUCANAS	JULIE	KENT	3	3		103	90
2017	202	131	5	CHULUCANAS	JULIE	KENT	3	4		180	90
2017	203	131	5	CHULUCANAS	JULIE	KENT	3	5		250	90
2017	204	131	5	CHULUCANAS	JULIE	KENT	3	6
2017	205	131	5	CHULUCANAS	JULIE	KENT	3	7		190	90
2017	206	131	5	CHULUCANAS	JULIE	KENT	3	8		140	90
2017	207	131	5	CHULUCANAS	JULIE	KENT	3	9		210	80
2017	208	241	3	CHULUCANAS	CHATO	KENT	3	1		190	80
2017	209	241	3	CHULUCANAS	CHATO	KENT	3	2		220	90
2017	210	241	3	CHULUCANAS	CHATO	KENT	3	3		140	80
2017	211	241	3	CHULUCANAS	CHATO	KENT	3	4		210	75
2017	212	241	3	CHULUCANAS	CHATO	KENT	3	5		200	90
2017	213	241	3	CHULUCANAS	CHATO	KENT	3	6		290	90
2017	214	241	3	CHULUCANAS	CHATO	KENT	3	7		140	50
2017	215	241	3	CHULUCANAS	CHATO	KENT	3	8		170	90
2017	216	241	3	CHULUCANAS	CHATO	KENT	3	9		160	85
2018	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1			95
2018	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2			95
2018	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	3		315
2018	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	4
2018	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	5		230
2018	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	6		232
2018	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	7			80
2018	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	8			90
2018	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	9			20
2018	10	231	2	CHATO	JULIE	KENT	1	1			80
2018	11	231	2	CHATO	JULIE	KENT	1	2			50
2018	12	231	2	CHATO	JULIE	KENT	1	3		92
2018	13	231	2	CHATO	JULIE	KENT	1	4		163
2018	14	231	2	CHATO	JULIE	KENT	1	5			5
2018	15	231	2	CHATO	JULIE	KENT	1	6		145
2018	16	231	2	CHATO	JULIE	KENT	1	7			80
2018	17	231	2	CHATO	JULIE	KENT	1	8			40
2018	18	231	2	CHATO	JULIE	KENT	1	9
2018	19	121	8	CHATO	CHULUCANAS	KENT	1	1
2018	20	121	8	CHATO	CHULUCANAS	KENT	1	2			90
2018	21	121	8	CHATO	CHULUCANAS	KENT	1	3			90
2018	22	121	8	CHATO	CHULUCANAS	KENT	1	4		295
2018	23	121	8	CHATO	CHULUCANAS	KENT	1	5		382
2018	24	121	8	CHATO	CHULUCANAS	KENT	1	6		270
2018	25	121	8	CHATO	CHULUCANAS	KENT	1	7			90
2018	26	121	8	CHATO	CHULUCANAS	KENT	1	8			90
2018	27	121	8	CHATO	CHULUCANAS	KENT	1	9			50
2018	28	211	4	CHATO	IRWIN	KENT	1	1			50
2018	29	211	4	CHATO	IRWIN	KENT	1	2			60
2018	30	211	4	CHATO	IRWIN	KENT	1	3		242
2018	31	211	4	CHATO	IRWIN	KENT	1	4			70
2018	32	211	4	CHATO	IRWIN	KENT	1	5		320
2018	33	211	4	CHATO	IRWIN	KENT	1	6		300
2018	34	211	4	CHATO	IRWIN	KENT	1	7			80
2018	35	211	4	CHATO	IRWIN	KENT	1	8			90
2018	36	211	4	CHATO	IRWIN	KENT	1	9			95
2018	37	131	5	CHULUCANAS	JULIE	KENT	1	1			25
2018	38	131	5	CHULUCANAS	JULIE	KENT	1	2		250
2018	39	131	5	CHULUCANAS	JULIE	KENT	1	3			80
2018	40	131	5	CHULUCANAS	JULIE	KENT	1	4		240
2018	41	131	5	CHULUCANAS	JULIE	KENT	1	5		200
2018	42	131	5	CHULUCANAS	JULIE	KENT	1	6			10
2018	43	131	5	CHULUCANAS	JULIE	KENT	1	7			75
2018	44	131	5	CHULUCANAS	JULIE	KENT	1	8			60
2018	45	131	5	CHULUCANAS	JULIE	KENT	1	9			90
2018	46	241	3	CHULUCANAS	CHATO	KENT	1	1			20
2018	47	241	3	CHULUCANAS	CHATO	KENT	1	2			40
2018	48	241	3	CHULUCANAS	CHATO	KENT	1	3			70
2018	49	241	3	CHULUCANAS	CHATO	KENT	1	4		250
2018	50	241	3	CHULUCANAS	CHATO	KENT	1	5		195
2018	51	241	3	CHULUCANAS	CHATO	KENT	1	6		280
2018	52	241	3	CHULUCANAS	CHATO	KENT	1	7			30
2018	53	241	3	CHULUCANAS	CHATO	KENT	1	8			80
2018	54	241	3	CHULUCANAS	CHATO	KENT	1	9			90
2018	55	111	6	CHULUCANAS	IRWIN	KENT	1	1			80
2018	56	111	6	CHULUCANAS	IRWIN	KENT	1	2		146
2018	57	111	6	CHULUCANAS	IRWIN	KENT	1	3			15
2018	58	111	6	CHULUCANAS	IRWIN	KENT	1	4		138
2018	59	111	6	CHULUCANAS	IRWIN	KENT	1	5			90
2018	60	111	6	CHULUCANAS	IRWIN	KENT	1	6		190
2018	61	111	6	CHULUCANAS	IRWIN	KENT	1	7
2018	62	111	6	CHULUCANAS	IRWIN	KENT	1	8			80
2018	63	111	6	CHULUCANAS	IRWIN	KENT	1	9			95
2018	64	221	7	CHATO	CHATO	KENT	1	1		180
2018	65	221	7	CHATO	CHATO	KENT	1	2			50
2018	66	221	7	CHATO	CHATO	KENT	1	3			80
2018	67	221	7	CHATO	CHATO	KENT	1	4			80
2018	68	221	7	CHATO	CHATO	KENT	1	5			90
2018	69	221	7	CHATO	CHATO	KENT	1	6		178
2018	70	221	7	CHATO	CHATO	KENT	1	7
2018	71	221	7	CHATO	CHATO	KENT	1	8		204
2018	72	221	7	CHATO	CHATO	KENT	1	9			97
2018	73	211	4	CHATO	IRWIN	KENT	2	1
2018	74	211	4	CHATO	IRWIN	KENT	2	2		266	90
2018	75	211	4	CHATO	IRWIN	KENT	2	3		187	85
2018	76	211	4	CHATO	IRWIN	KENT	2	4			5
2018	77	211	4	CHATO	IRWIN	KENT	2	5		220	90
2018	78	211	4	CHATO	IRWIN	KENT	2	6			35
2018	79	211	4	CHATO	IRWIN	KENT	2	7			90
2018	80	211	4	CHATO	IRWIN	KENT	2	8			95
2018	81	211	4	CHATO	IRWIN	KENT	2	9			90
2018	82	121	8	CHATO	CHULUCANAS	KENT	2	1			85
2018	83	121	8	CHATO	CHULUCANAS	KENT	2	2		108	90
2018	84	121	8	CHATO	CHULUCANAS	KENT	2	3		200	90
2018	85	121	8	CHATO	CHULUCANAS	KENT	2	4			95
2018	86	121	8	CHATO	CHULUCANAS	KENT	2	5			95
2018	87	121	8	CHATO	CHULUCANAS	KENT	2	6			90
2018	88	121	8	CHATO	CHULUCANAS	KENT	2	7		280	85
2018	89	121	8	CHATO	CHULUCANAS	KENT	2	8			90
2018	90	121	8	CHATO	CHULUCANAS	KENT	2	9			90
2018	91	231	2	CHATO	JULIE	KENT	2	1
2018	92	231	2	CHATO	JULIE	KENT	2	2		300	85
2018	93	231	2	CHATO	JULIE	KENT	2	3		290	80
2018	94	231	2	CHATO	JULIE	KENT	2	4			50
2018	95	231	2	CHATO	JULIE	KENT	2	5		214	90
2018	96	231	2	CHATO	JULIE	KENT	2	6			90
2018	97	231	2	CHATO	JULIE	KENT	2	7			30
2018	98	231	2	CHATO	JULIE	KENT	2	8			85
2018	99	231	2	CHATO	JULIE	KENT	2	9			85
2018	100	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1		305	85
2018	101	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2			85
2018	102	141	1	CHULUCANAS	CHULUCANAS	KENT	2	3			75
2018	103	141	1	CHULUCANAS	CHULUCANAS	KENT	2	4			80
2018	104	141	1	CHULUCANAS	CHULUCANAS	KENT	2	5		230	75
2018	105	141	1	CHULUCANAS	CHULUCANAS	KENT	2	6		150	95
2018	106	141	1	CHULUCANAS	CHULUCANAS	KENT	2	7			80
2018	107	141	1	CHULUCANAS	CHULUCANAS	KENT	2	8			95
2018	108	141	1	CHULUCANAS	CHULUCANAS	KENT	2	9			80
2018	109	221	7	CHATO	CHATO	KENT	2	1			60
2018	110	221	7	CHATO	CHATO	KENT	2	2			80
2018	111	221	7	CHATO	CHATO	KENT	2	3			5
2018	112	221	7	CHATO	CHATO	KENT	2	4			3
2018	113	221	7	CHATO	CHATO	KENT	2	5		150	70
2018	114	221	7	CHATO	CHATO	KENT	2	6		140	30
2018	115	221	7	CHATO	CHATO	KENT	2	7			0
2018	116	221	7	CHATO	CHATO	KENT	2	8		150	10
2018	117	221	7	CHATO	CHATO	KENT	2	9			80
2018	118	111	6	CHULUCANAS	IRWIN	KENT	2	1
2018	119	111	6	CHULUCANAS	IRWIN	KENT	2	2			95
2018	120	111	6	CHULUCANAS	IRWIN	KENT	2	3			90
2018	121	111	6	CHULUCANAS	IRWIN	KENT	2	4			85
2018	122	111	6	CHULUCANAS	IRWIN	KENT	2	5			10
2018	123	111	6	CHULUCANAS	IRWIN	KENT	2	6			5
2018	124	111	6	CHULUCANAS	IRWIN	KENT	2	7		160	70
2018	125	111	6	CHULUCANAS	IRWIN	KENT	2	8		325	90
2018	126	111	6	CHULUCANAS	IRWIN	KENT	2	9		265	95
2018	127	241	3	CHULUCANAS	CHATO	KENT	2	1			80
2018	128	241	3	CHULUCANAS	CHATO	KENT	2	2			20
2018	129	241	3	CHULUCANAS	CHATO	KENT	2	3		65	20
2018	130	241	3	CHULUCANAS	CHATO	KENT	2	4		61	20
2018	131	241	3	CHULUCANAS	CHATO	KENT	2	5
2018	132	241	3	CHULUCANAS	CHATO	KENT	2	6		255	95
2018	133	241	3	CHULUCANAS	CHATO	KENT	2	7			40
2018	134	241	3	CHULUCANAS	CHATO	KENT	2	8			60
2018	135	241	3	CHULUCANAS	CHATO	KENT	2	9			50
2018	136	131	5	CHULUCANAS	JULIE	KENT	2	1			20
2018	137	131	5	CHULUCANAS	JULIE	KENT	2	2			95
2018	138	131	5	CHULUCANAS	JULIE	KENT	2	3		124	90
2018	139	131	5	CHULUCANAS	JULIE	KENT	2	4		220	90
2018	140	131	5	CHULUCANAS	JULIE	KENT	2	5		260	95
2018	141	131	5	CHULUCANAS	JULIE	KENT	2	6
2018	142	131	5	CHULUCANAS	JULIE	KENT	2	7			90
2018	143	131	5	CHULUCANAS	JULIE	KENT	2	8			95
2018	144	131	5	CHULUCANAS	JULIE	KENT	2	9			95
2018	145	121	8	CHATO	CHULUCANAS	KENT	3	1			95
2018	146	121	8	CHATO	CHULUCANAS	KENT	3	2			95
2018	147	121	8	CHATO	CHULUCANAS	KENT	3	3			95
2018	148	121	8	CHATO	CHULUCANAS	KENT	3	4		110	90
2018	149	121	8	CHATO	CHULUCANAS	KENT	3	5		147	95
2018	150	121	8	CHATO	CHULUCANAS	KENT	3	6			100
2018	151	121	8	CHATO	CHULUCANAS	KENT	3	7			75
2018	152	121	8	CHATO	CHULUCANAS	KENT	3	8		390	90
2018	153	121	8	CHATO	CHULUCANAS	KENT	3	9			95
2018	154	211	4	CHATO	IRWIN	KENT	3	1			80
2018	155	211	4	CHATO	IRWIN	KENT	3	2			95
2018	156	211	4	CHATO	IRWIN	KENT	3	3			40
2018	157	211	4	CHATO	IRWIN	KENT	3	4		160	65
2018	158	211	4	CHATO	IRWIN	KENT	3	5
2018	159	211	4	CHATO	IRWIN	KENT	3	6		126	75
2018	160	211	4	CHATO	IRWIN	KENT	3	7			20
2018	161	211	4	CHATO	IRWIN	KENT	3	8		136	25
2018	162	211	4	CHATO	IRWIN	KENT	3	9			40
2018	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1		390	80
2018	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2			95
2018	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	3			90
2018	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	4			70
2018	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	5		250	95
2018	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	6			95
2018	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	7			0
2018	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	8		272	90
2018	171	141	1	CHULUCANAS	CHULUCANAS	KENT	3	9			95
2018	172	231	2	CHATO	JULIE	KENT	3	1		203	50
2018	173	231	2	CHATO	JULIE	KENT	3	2			0
2018	174	231	2	CHATO	JULIE	KENT	3	3			0
2018	175	231	2	CHATO	JULIE	KENT	3	4			5
2018	176	231	2	CHATO	JULIE	KENT	3	5			2
2018	177	231	2	CHATO	JULIE	KENT	3	6		33	15
2018	178	231	2	CHATO	JULIE	KENT	3	7		116	40
2018	179	231	2	CHATO	JULIE	KENT	3	8			60
2018	180	231	2	CHATO	JULIE	KENT	3	9			50
2018	181	111	6	CHULUCANAS	IRWIN	KENT	3	1			90
2018	182	111	6	CHULUCANAS	IRWIN	KENT	3	2			95
2018	183	111	6	CHULUCANAS	IRWIN	KENT	3	3		245	90
2018	184	111	6	CHULUCANAS	IRWIN	KENT	3	4		290	90
2018	185	111	6	CHULUCANAS	IRWIN	KENT	3	5			70
2018	186	111	6	CHULUCANAS	IRWIN	KENT	3	6			85
2018	187	111	6	CHULUCANAS	IRWIN	KENT	3	7			90
2018	188	111	6	CHULUCANAS	IRWIN	KENT	3	8			80
2018	189	111	6	CHULUCANAS	IRWIN	KENT	3	9		160	50
2018	190	221	7	CHATO	CHATO	KENT	3	1		220	80
2018	191	221	7	CHATO	CHATO	KENT	3	2			10
2018	192	221	7	CHATO	CHATO	KENT	3	3			0
2018	193	221	7	CHATO	CHATO	KENT	3	4		182	50
2018	194	221	7	CHATO	CHATO	KENT	3	5			0
2018	195	221	7	CHATO	CHATO	KENT	3	6
2018	196	221	7	CHATO	CHATO	KENT	3	7		180	80
2018	197	221	7	CHATO	CHATO	KENT	3	8			5
2018	198	221	7	CHATO	CHATO	KENT	3	9			2
2018	199	131	5	CHULUCANAS	JULIE	KENT	3	1
2018	200	131	5	CHULUCANAS	JULIE	KENT	3	2			85
2018	201	131	5	CHULUCANAS	JULIE	KENT	3	3			70
2018	202	131	5	CHULUCANAS	JULIE	KENT	3	4		213	70
2018	203	131	5	CHULUCANAS	JULIE	KENT	3	5		300	90
2018	204	131	5	CHULUCANAS	JULIE	KENT	3	6
2018	205	131	5	CHULUCANAS	JULIE	KENT	3	7			10
2018	206	131	5	CHULUCANAS	JULIE	KENT	3	8			20
2018	207	131	5	CHULUCANAS	JULIE	KENT	3	9		200	60
2018	208	241	3	CHULUCANAS	CHATO	KENT	3	1			80
2018	209	241	3	CHULUCANAS	CHATO	KENT	3	2			25
2018	210	241	3	CHULUCANAS	CHATO	KENT	3	3			60
2018	211	241	3	CHULUCANAS	CHATO	KENT	3	4		210	60
2018	212	241	3	CHULUCANAS	CHATO	KENT	3	5		75	15
2018	213	241	3	CHULUCANAS	CHATO	KENT	3	6		188	40
2018	214	241	3	CHULUCANAS	CHATO	KENT	3	7			10
2018	215	241	3	CHULUCANAS	CHATO	KENT	3	8			80
2018	216	241	3	CHULUCANAS	CHATO	KENT	3	9			40
2019	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	3.15	180	95	80
2019	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	3.60	280	90	75
2019	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	3	3.65	234	80	70
2019	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	4
2019	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	5	3.00	110	90	40
2019	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	6	3.40	202	95	80
2019	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	7	3.40	202	80	75
2019	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	8	3.20	108	70	50
2019	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	9	2.70	80	90	0
2019	10	231	2	CHATO	JULIE	KENT	1	1	3.65	245	85	5
2019	11	231	2	CHATO	JULIE	KENT	1	2	3.95	228	80	25
2019	12	231	2	CHATO	JULIE	KENT	1	3	4.85	320	95	20
2019	13	231	2	CHATO	JULIE	KENT	1	4	4.35	307	90	40
2019	14	231	2	CHATO	JULIE	KENT	1	5	4.00	251	80	75
2019	15	231	2	CHATO	JULIE	KENT	1	6	4.20	264	70	35
2019	16	231	2	CHATO	JULIE	KENT	1	7	4.25	280	95	65
2019	17	231	2	CHATO	JULIE	KENT	1	8	4.80	288	95	80
2019	18	231	2	CHATO	JULIE	KENT	1	9				70
2019	19	121	8	CHATO	CHULUCANAS	KENT	1	1	4.05			70
2019	20	121	8	CHATO	CHULUCANAS	KENT	1	2	4.00	122	90	80
2019	21	121	8	CHATO	CHULUCANAS	KENT	1	3	3.70	118	90	80
2019	22	121	8	CHATO	CHULUCANAS	KENT	1	4	4.25	152	90	75
2019	23	121	8	CHATO	CHULUCANAS	KENT	1	5	4.35	333	90	40
2019	24	121	8	CHATO	CHULUCANAS	KENT	1	6	3.70	221	95	75
2019	25	121	8	CHATO	CHULUCANAS	KENT	1	7	4.50	304	85	90
2019	26	121	8	CHATO	CHULUCANAS	KENT	1	8	4.15	182	80	90
2019	27	121	8	CHATO	CHULUCANAS	KENT	1	9	3.95	210	95	70
2019	28	211	4	CHATO	IRWIN	KENT	1	1	4.20	395	90	30
2019	29	211	4	CHATO	IRWIN	KENT	1	2	3.95	160	90	75
2019	30	211	4	CHATO	IRWIN	KENT	1	3	4.15	226	90	70
2019	31	211	4	CHATO	IRWIN	KENT	1	4	4.45	194	85	70
2019	32	211	4	CHATO	IRWIN	KENT	1	5	4.10	315	75	60
2019	33	211	4	CHATO	IRWIN	KENT	1	6	4.45	130	65	30
2019	34	211	4	CHATO	IRWIN	KENT	1	7	4.40	175	80	30
2019	35	211	4	CHATO	IRWIN	KENT	1	8	4.00	140	80	60
2019	36	211	4	CHATO	IRWIN	KENT	1	9	3.80	97	65	60
2019	37	131	5	CHULUCANAS	JULIE	KENT	1	1	3.00	125	90	80
2019	38	131	5	CHULUCANAS	JULIE	KENT	1	2	3.95	185	70	65
2019	39	131	5	CHULUCANAS	JULIE	KENT	1	3	4.10	203	80	90
2019	40	131	5	CHULUCANAS	JULIE	KENT	1	4	3.65	160	90	45
2019	41	131	5	CHULUCANAS	JULIE	KENT	1	5	3.65	137	90	60
2019	42	131	5	CHULUCANAS	JULIE	KENT	1	6	3.10	138	95	65
2019	43	131	5	CHULUCANAS	JULIE	KENT	1	7	3.70	184	95	80
2019	44	131	5	CHULUCANAS	JULIE	KENT	1	8	3.60	144	90	80
2019	45	131	5	CHULUCANAS	JULIE	KENT	1	9	3.80	210	85	75
2019	46	241	3	CHULUCANAS	CHATO	KENT	1	1	4.45	219	90	80
2019	47	241	3	CHULUCANAS	CHATO	KENT	1	2	4.65	240	90	70
2019	48	241	3	CHULUCANAS	CHATO	KENT	1	3	4.25	265	95	85
2019	49	241	3	CHULUCANAS	CHATO	KENT	1	4	4.35	215	95	75
2019	50	241	3	CHULUCANAS	CHATO	KENT	1	5	4.45	361	90	75
2019	51	241	3	CHULUCANAS	CHATO	KENT	1	6	4.05	144	80	75
2019	52	241	3	CHULUCANAS	CHATO	KENT	1	7	4.35	215	85	70
2019	53	241	3	CHULUCANAS	CHATO	KENT	1	8	4.10	104	90	45
2019	54	241	3	CHULUCANAS	CHATO	KENT	1	9	4.05	97	70	75
2019	55	111	6	CHULUCANAS	IRWIN	KENT	1	1	4.30	76	60	40
2019	56	111	6	CHULUCANAS	IRWIN	KENT	1	2	3.65	220	90	65
2019	57	111	6	CHULUCANAS	IRWIN	KENT	1	3	4.10	245	80	60
2019	58	111	6	CHULUCANAS	IRWIN	KENT	1	4	3.70	220	95	70
2019	59	111	6	CHULUCANAS	IRWIN	KENT	1	5	3.40	98	80	30
2019	60	111	6	CHULUCANAS	IRWIN	KENT	1	6	3.85	172	95	80
2019	61	111	6	CHULUCANAS	IRWIN	KENT	1	7
2019	62	111	6	CHULUCANAS	IRWIN	KENT	1	8	3.45	148	80	80
2019	63	111	6	CHULUCANAS	IRWIN	KENT	1	9
2019	64	221	7	CHATO	CHATO	KENT	1	1	4.45	206	95	20
2019	65	221	7	CHATO	CHATO	KENT	1	2	3.65	260	95	50
2019	66	221	7	CHATO	CHATO	KENT	1	3	3.40	132	95	70
2019	67	221	7	CHATO	CHATO	KENT	1	4	3.15	25	70
2019	68	221	7	CHATO	CHATO	KENT	1	5	3.35	51	80	70
2019	69	221	7	CHATO	CHATO	KENT	1	6	4.25	210	95	70
2019	70	221	7	CHATO	CHATO	KENT	1	7	3.30	65	80	80
2019	71	221	7	CHATO	CHATO	KENT	1	8	3.85	217	80	70
2019	72	221	7	CHATO	CHATO	KENT	1	9	3.00	65	85	70
2019	73	211	4	CHATO	IRWIN	KENT	2	1	4.85	23	30	20
2019	74	211	4	CHATO	IRWIN	KENT	2	2	4.05	164	90	70
2019	75	211	4	CHATO	IRWIN	KENT	2	3	3.95	187	70	70
2019	76	211	4	CHATO	IRWIN	KENT	2	4	3.10	34	70	40
2019	77	211	4	CHATO	IRWIN	KENT	2	5	3.05	167	80	65
2019	78	211	4	CHATO	IRWIN	KENT	2	6	3.40	130	90	45
2019	79	211	4	CHATO	IRWIN	KENT	2	7	4.05	312	60	60
2019	80	211	4	CHATO	IRWIN	KENT	2	8	3.75	136	60	40
2019	81	211	4	CHATO	IRWIN	KENT	2	9	3.70	139	85	65
2019	82	121	8	CHATO	CHULUCANAS	KENT	2	1	3.45	185	85	70
2019	83	121	8	CHATO	CHULUCANAS	KENT	2	2	3.75	58	90	50
2019	84	121	8	CHATO	CHULUCANAS	KENT	2	3	3.70	118	90	65
2019	85	121	8	CHATO	CHULUCANAS	KENT	2	4	3.75	152	70	60
2019	86	121	8	CHATO	CHULUCANAS	KENT	2	5	3.65	75	90
2019	87	121	8	CHATO	CHULUCANAS	KENT	2	6	4.05	187	90	70
2019	88	121	8	CHATO	CHULUCANAS	KENT	2	7	3.95	120	80	80
2019	89	121	8	CHATO	CHULUCANAS	KENT	2	8	3.40	150	90	40
2019	90	121	8	CHATO	CHULUCANAS	KENT	2	9	2.90	87	70	70
2019	91	231	2	CHATO	JULIE	KENT	2	1	2.00
2019	92	231	2	CHATO	JULIE	KENT	2	2	3.90	142	95	70
2019	93	231	2	CHATO	JULIE	KENT	2	3	3.90	260	90	75
2019	94	231	2	CHATO	JULIE	KENT	2	4	3.10	124	90	20
2019	95	231	2	CHATO	JULIE	KENT	2	5	3.60	132	80	70
2019	96	231	2	CHATO	JULIE	KENT	2	6	3.85	103	60	65
2019	97	231	2	CHATO	JULIE	KENT	2	7	3.65	104	85	65
2019	98	231	2	CHATO	JULIE	KENT	2	8	3.70	123	60	80
2019	99	231	2	CHATO	JULIE	KENT	2	9	3.35	139	70	90
2019	100	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	3.85	260	90	75
2019	101	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	3.55	215	90	35
2019	102	141	1	CHULUCANAS	CHULUCANAS	KENT	2	3	3.80	103	90	75
2019	103	141	1	CHULUCANAS	CHULUCANAS	KENT	2	4	3.45	181	90	65
2019	104	141	1	CHULUCANAS	CHULUCANAS	KENT	2	5	4.15	360	80	80
2019	105	141	1	CHULUCANAS	CHULUCANAS	KENT	2	6	3.45	98	60	20
2019	106	141	1	CHULUCANAS	CHULUCANAS	KENT	2	7	3.40	229	95	70
2019	107	141	1	CHULUCANAS	CHULUCANAS	KENT	2	8	3.30	159	90	70
2019	108	141	1	CHULUCANAS	CHULUCANAS	KENT	2	9	3.40	211	90	60
2019	109	221	7	CHATO	CHATO	KENT	2	1	3.35	88	70	40
2019	110	221	7	CHATO	CHATO	KENT	2	2	3.15	164	80	50
2019	111	221	7	CHATO	CHATO	KENT	2	3	3.30	127	70	50
2019	112	221	7	CHATO	CHATO	KENT	2	4	4.25	130	90	30
2019	113	221	7	CHATO	CHATO	KENT	2	5	2.80	218	70	65
2019	114	221	7	CHATO	CHATO	KENT	2	6	4.35	280	90	50
2019	115	221	7	CHATO	CHATO	KENT	2	7	3.95	125	80	50
2019	116	221	7	CHATO	CHATO	KENT	2	8	4.35	150	80	40
2019	117	221	7	CHATO	CHATO	KENT	2	9	3.50	102	80	50
2019	118	111	6	CHULUCANAS	IRWIN	KENT	2	1
2019	119	111	6	CHULUCANAS	IRWIN	KENT	2	2	3.30
2019	120	111	6	CHULUCANAS	IRWIN	KENT	2	3	3.20	95	90	60
2019	121	111	6	CHULUCANAS	IRWIN	KENT	2	4	3.60	134	70	60
2019	122	111	6	CHULUCANAS	IRWIN	KENT	2	5	3.70	205	70	30
2019	123	111	6	CHULUCANAS	IRWIN	KENT	2	6	4.10	152	90	30
2019	124	111	6	CHULUCANAS	IRWIN	KENT	2	7	3.90	125	90	60
2019	125	111	6	CHULUCANAS	IRWIN	KENT	2	8	4.20	219	70	80
2019	126	111	6	CHULUCANAS	IRWIN	KENT	2	9	3.50	241	80	80
2019	127	241	3	CHULUCANAS	CHATO	KENT	2	1	3.30	172	60
2019	128	241	3	CHULUCANAS	CHATO	KENT	2	2	3.50	220	85	75
2019	129	241	3	CHULUCANAS	CHATO	KENT	2	3	3.85	152	90	70
2019	130	241	3	CHULUCANAS	CHATO	KENT	2	4	3.95	130	90	50
2019	131	241	3	CHULUCANAS	CHATO	KENT	2	5	1.90	3	60	90
2019	132	241	3	CHULUCANAS	CHATO	KENT	2	6	1.85	216	80	70
2019	133	241	3	CHULUCANAS	CHATO	KENT	2	7	4.30	181	90	90
2019	134	241	3	CHULUCANAS	CHATO	KENT	2	8	3.45	176	80	70
2019	135	241	3	CHULUCANAS	CHATO	KENT	2	9	2.75	75	90	10
2019	136	131	5	CHULUCANAS	JULIE	KENT	2	1	2.75	120	90	70
2019	137	131	5	CHULUCANAS	JULIE	KENT	2	2	3.50	151	50	70
2019	138	131	5	CHULUCANAS	JULIE	KENT	2	3	3.40	135	80	80
2019	139	131	5	CHULUCANAS	JULIE	KENT	2	4	3.65	250	70	80
2019	140	131	5	CHULUCANAS	JULIE	KENT	2	5	3.10	188	70	75
2019	141	131	5	CHULUCANAS	JULIE	KENT	2	6
2019	142	131	5	CHULUCANAS	JULIE	KENT	2	7	2.90	121	90	40
2019	143	131	5	CHULUCANAS	JULIE	KENT	2	8	3.45	138	70	80
2019	144	131	5	CHULUCANAS	JULIE	KENT	2	9	2.90	145	90	70
2019	145	121	8	CHATO	CHULUCANAS	KENT	3	1	3.20	271	50	50
2019	146	121	8	CHATO	CHULUCANAS	KENT	3	2	3.40	221	40	60
2019	147	121	8	CHATO	CHULUCANAS	KENT	3	3	3.60	208	80	70
2019	148	121	8	CHATO	CHULUCANAS	KENT	3	4	3.30	168	50	45
2019	149	121	8	CHATO	CHULUCANAS	KENT	3	5	3.00	42	7	15
2019	150	121	8	CHATO	CHULUCANAS	KENT	3	6	2.85	150	50	65
2019	151	121	8	CHATO	CHULUCANAS	KENT	3	7	3.65	214	70	75
2019	152	121	8	CHATO	CHULUCANAS	KENT	3	8	3.65	346	70	80
2019	153	121	8	CHATO	CHULUCANAS	KENT	3	9	3.00	190	50	60
2019	154	211	4	CHATO	IRWIN	KENT	3	1	4.20	400	90	80
2019	155	211	4	CHATO	IRWIN	KENT	3	2	3.30	183	60
2019	156	211	4	CHATO	IRWIN	KENT	3	3	4.30	263	90	10
2019	157	211	4	CHATO	IRWIN	KENT	3	4	3.30	250	55	70
2019	158	211	4	CHATO	IRWIN	KENT	3	5
2019	159	211	4	CHATO	IRWIN	KENT	3	6	3.65	262	90	65
2019	160	211	4	CHATO	IRWIN	KENT	3	7	3.65	382	90	30
2019	161	211	4	CHATO	IRWIN	KENT	3	8	4.00	475	90	30
2019	162	211	4	CHATO	IRWIN	KENT	3	9	4.05	393	90	60
2019	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	4.15	470	90	70
2019	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	4.20	287	80	80
2019	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	3	4.15	414	80	80
2019	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	4	3.16	497	75	80
2019	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	5	3.85	279	90	75
2019	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	6	3.40	179	50	75
2019	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	7	3.40	208	95	20
2019	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	8	3.20	256	90	70
2019	171	141	1	CHULUCANAS	CHULUCANAS	KENT	3	9	2.80	104	80	75
2019	172	231	2	CHATO	JULIE	KENT	3	1	4.45	539	90	30
2019	173	231	2	CHATO	JULIE	KENT	3	2	3.70	402	90	20
2019	174	231	2	CHATO	JULIE	KENT	3	3	3.85	310	90	20
2019	175	231	2	CHATO	JULIE	KENT	3	4	3.30	103	90	50
2019	176	231	2	CHATO	JULIE	KENT	3	5	3.40	50	60	50
2019	177	231	2	CHATO	JULIE	KENT	3	6	3.40	200	80	10
2019	178	231	2	CHATO	JULIE	KENT	3	7	3.95	324	90	75
2019	179	231	2	CHATO	JULIE	KENT	3	8	4.05	354	90	75
2019	180	231	2	CHATO	JULIE	KENT	3	9	3.25	135	70	70
2019	181	111	6	CHULUCANAS	IRWIN	KENT	3	1	2.80	130	80	70
2019	182	111	6	CHULUCANAS	IRWIN	KENT	3	2	3.00	140	80	40
2019	183	111	6	CHULUCANAS	IRWIN	KENT	3	3	3.65	284	80	80
2019	184	111	6	CHULUCANAS	IRWIN	KENT	3	4	3.65	261	90	80
2019	185	111	6	CHULUCANAS	IRWIN	KENT	3	5	3.75	220	95	20
2019	186	111	6	CHULUCANAS	IRWIN	KENT	3	6	3.15	175	70	80
2019	187	111	6	CHULUCANAS	IRWIN	KENT	3	7	3.60	310	95	70
2019	188	111	6	CHULUCANAS	IRWIN	KENT	3	8	3.60	440	90	70
2019	189	111	6	CHULUCANAS	IRWIN	KENT	3	9	4.20	415	90	70
2019	190	221	7	CHATO	CHATO	KENT	3	1	3.70	360	50	70
2019	191	221	7	CHATO	CHATO	KENT	3	2	4.70	230	90	15
2019	192	221	7	CHATO	CHATO	KENT	3	3	4.10	350	90	20
2019	193	221	7	CHATO	CHATO	KENT	3	4	4.00	423	90	70
2019	194	221	7	CHATO	CHATO	KENT	3	5	3.50	304	90	60
2019	195	221	7	CHATO	CHATO	KENT	3	6
2019	196	221	7	CHATO	CHATO	KENT	3	7	4.00	410	90	80
2019	197	221	7	CHATO	CHATO	KENT	3	8	3.90	380	90	80
2019	198	221	7	CHATO	CHATO	KENT	3	9	4.05	340	90	50
2019	199	131	5	CHULUCANAS	JULIE	KENT	3	1
2019	200	131	5	CHULUCANAS	JULIE	KENT	3	2	3.70	268	80	80
2019	201	131	5	CHULUCANAS	JULIE	KENT	3	3	3.40	190	85	80
2019	202	131	5	CHULUCANAS	JULIE	KENT	3	4	3.75	214	80	80
2019	203	131	5	CHULUCANAS	JULIE	KENT	3	5	3.70	380	90	80
2019	204	131	5	CHULUCANAS	JULIE	KENT	3	6		214
2019	205	131	5	CHULUCANAS	JULIE	KENT	3	7	3.60	300	95	80
2019	206	131	5	CHULUCANAS	JULIE	KENT	3	8	3.40	325	90	60
2019	207	131	5	CHULUCANAS	JULIE	KENT	3	9	3.70	350	90	70
2019	208	241	3	CHULUCANAS	CHATO	KENT	3	1	3.50	180	80	75
2019	209	241	3	CHULUCANAS	CHATO	KENT	3	2	4.40	295	90	25
2019	210	241	3	CHULUCANAS	CHATO	KENT	3	3	3.60	232	70	50
2019	211	241	3	CHULUCANAS	CHATO	KENT	3	4	4.05	250	70	80
2019	212	241	3	CHULUCANAS	CHATO	KENT	3	5	4.10	340	90	80
2019	213	241	3	CHULUCANAS	CHATO	KENT	3	6	4.05	382	80	80
2019	214	241	3	CHULUCANAS	CHATO	KENT	3	7	3.70	379	90	70
2019	215	241	3	CHULUCANAS	CHATO	KENT	3	8	3.50	120	80	70
2019	216	241	3	CHULUCANAS	CHATO	KENT	3	9	3.65	260	90	70

fru %>% kable(caption = "Evaluation of mango fruit quality", align = 'c')

Evaluation of mango fruit quality

year	n	treat	n_treat	stock	edge	scion	block	n_plant	sample	n_fruits	weigth	long	diameter_1	diameter_2	diameter_average
2023	1	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	1	26	270	86	78	74	76.0
2023	2	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	2	26	520	111	90	83	86.5
2023	3	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	3	26	385	103	86	75	80.5
2023	4	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	4	26	230	86	70	69	69.5
2023	5	141	1	CHULUCANAS	CHULUCANAS	KENT	1	1	5	26	285	88	78	71	74.5
2023	6	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	1	35	600	116	96	87	91.5
2023	7	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	2	35	457	101	88	88	88.0
2023	8	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	3	35	520	115	88	81	84.5
2023	9	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	4	35	665	126	103	95	99.0
2023	10	141	1	CHULUCANAS	CHULUCANAS	KENT	1	2	5	35	305	91	78	80	79.0
2023	11	231	2	CHATO	JULIE	KENT	1	1	1	118	422	100	91	83	87.0
2023	12	231	2	CHATO	JULIE	KENT	1	1	2	118	377	102	84	76	80.0
2023	13	231	2	CHATO	JULIE	KENT	1	1	3	118	300	91	84	72	78.0
2023	14	231	2	CHATO	JULIE	KENT	1	1	4	118	455	106	88	82	85.0
2023	15	231	2	CHATO	JULIE	KENT	1	1	5	118	485	108	91	83	87.0
2023	16	231	2	CHATO	JULIE	KENT	1	2	1	102	420	104	87	83	85.0
2023	17	231	2	CHATO	JULIE	KENT	1	2	2	102	395	99	86	80	83.0
2023	18	231	2	CHATO	JULIE	KENT	1	2	3	102	335	98	87	70	78.5
2023	19	231	2	CHATO	JULIE	KENT	1	2	4	102	610	118	99	92	95.5
2023	20	231	2	CHATO	JULIE	KENT	1	2	5	102	530	109	98	90	94.0
2023	21	241	3	CHULUCANAS	CHATO	KENT	1	1	1	86	485	105	90	83	86.5
2023	22	241	3	CHULUCANAS	CHATO	KENT	1	1	2	86	460	103	89	80	84.5
2023	23	241	3	CHULUCANAS	CHATO	KENT	1	1	3	86	576	111	101	86	93.5
2023	24	241	3	CHULUCANAS	CHATO	KENT	1	1	4	86	610	114	97	91	94.0
2023	25	241	3	CHULUCANAS	CHATO	KENT	1	1	5	86	505	111	92	85	88.5
2023	26	241	3	CHULUCANAS	CHATO	KENT	1	2	1	105	475	109	88	84	86.0
2023	27	241	3	CHULUCANAS	CHATO	KENT	1	2	2	105	350	95	83	77	80.0
2023	28	241	3	CHULUCANAS	CHATO	KENT	1	2	3	105	345	96	86	74	80.0
2023	29	241	3	CHULUCANAS	CHATO	KENT	1	2	4	105	475	101	95	86	90.5
2023	30	241	3	CHULUCANAS	CHATO	KENT	1	2	5	105	490	111	97	83	90.0
2023	31	211	4	CHATO	IRWIN	KENT	1	1	1	16	560	104	99	90	94.5
2023	32	211	4	CHATO	IRWIN	KENT	1	1	2	16	381	96	83	82	82.5
2023	33	211	4	CHATO	IRWIN	KENT	1	1	3	16	556	108	95	89	92.0
2023	34	211	4	CHATO	IRWIN	KENT	1	1	4	16	432	100	89	82	85.5
2023	35	211	4	CHATO	IRWIN	KENT	1	1	5	16	475	103	89	81	85.0
2023	36	211	4	CHATO	IRWIN	KENT	1	2	1	97	600	112	99	89	94.0
2023	37	211	4	CHATO	IRWIN	KENT	1	2	2	97	485	108	93	85	89.0
2023	38	211	4	CHATO	IRWIN	KENT	1	2	3	97	500	108	91	88	89.5
2023	39	211	4	CHATO	IRWIN	KENT	1	2	4	97	550	111	96	86	91.0
2023	40	211	4	CHATO	IRWIN	KENT	1	2	5	97	695	119	106	94	100.0
2023	41	131	5	CHULUCANAS	JULIE	KENT	1	1	1	94	367	100	82	80	81.0
2023	42	131	5	CHULUCANAS	JULIE	KENT	1	1	2	94	448	110	89	78	83.5
2023	43	131	5	CHULUCANAS	JULIE	KENT	1	1	3	94	400	104	87	77	82.0
2023	44	131	5	CHULUCANAS	JULIE	KENT	1	1	4	94	409	106	86	78	82.0
2023	45	131	5	CHULUCANAS	JULIE	KENT	1	1	5	94	402	102	90	80	85.0
2023	46	131	5	CHULUCANAS	JULIE	KENT	1	2	1	118	460	107	86	80	83.0
2023	47	131	5	CHULUCANAS	JULIE	KENT	1	2	2	118	450	107	94	81	87.5
2023	48	131	5	CHULUCANAS	JULIE	KENT	1	2	3	118	416	108	82	77	79.5
2023	49	131	5	CHULUCANAS	JULIE	KENT	1	2	4	118	500	107	97	85	91.0
2023	50	131	5	CHULUCANAS	JULIE	KENT	1	2	5	118	465	112	93	82	87.5
2023	51	111	6	CHULUCANAS	IRWIN	KENT	1	1	1	91	605	120	94	91	92.5
2023	52	111	6	CHULUCANAS	IRWIN	KENT	1	1	2	91	585	122	93	86	89.5
2023	53	111	6	CHULUCANAS	IRWIN	KENT	1	1	3	91	415	106	85	79	82.0
2023	54	111	6	CHULUCANAS	IRWIN	KENT	1	1	4	91	460	103	89	81	85.0
2023	55	111	6	CHULUCANAS	IRWIN	KENT	1	1	5	91	455	105	86	82	84.0
2023	56	111	6	CHULUCANAS	IRWIN	KENT	1	2	1	71	375	96	88	79	83.5
2023	57	111	6	CHULUCANAS	IRWIN	KENT	1	2	2	71	455	108	94	84	89.0
2023	58	111	6	CHULUCANAS	IRWIN	KENT	1	2	3	71	410	101	84	77	80.5
2023	59	111	6	CHULUCANAS	IRWIN	KENT	1	2	4	71	460	110	89	79	84.0
2023	60	111	6	CHULUCANAS	IRWIN	KENT	1	2	5	71	385	105	82	78	80.0
2023	61	221	7	CHATO	CHATO	KENT	1	1	1	13	440	100	90	81	85.5
2023	62	221	7	CHATO	CHATO	KENT	1	1	2	13	450	104	90	84	87.0
2023	63	221	7	CHATO	CHATO	KENT	1	1	3	13	540	110	91	88	89.5
2023	64	221	7	CHATO	CHATO	KENT	1	1	4	13	368	97	82	81	81.5
2023	65	221	7	CHATO	CHATO	KENT	1	1	5	13	341	93	85	78	81.5
2023	66	221	7	CHATO	CHATO	KENT	1	2	1	31	500	117	92	79	85.5
2023	67	221	7	CHATO	CHATO	KENT	1	2	2	31	305	96	81	71	76.0
2023	68	221	7	CHATO	CHATO	KENT	1	2	3	31	425	103	89	82	85.5
2023	69	221	7	CHATO	CHATO	KENT	1	2	4	31	360	101	82	78	80.0
2023	70	221	7	CHATO	CHATO	KENT	1	2	5	31	380	101	84	78	81.0
2023	71	121	8	CHATO	CHULUCANAS	KENT	1	1	1	29	340	95	80	77	78.5
2023	72	121	8	CHATO	CHULUCANAS	KENT	1	1	2	29	390	100	82	77	79.5
2023	73	121	8	CHATO	CHULUCANAS	KENT	1	1	3	29	480	110	92	81	86.5
2023	74	121	8	CHATO	CHULUCANAS	KENT	1	1	4	29	440	102	87	80	83.5
2023	75	121	8	CHATO	CHULUCANAS	KENT	1	1	5	29	450	104	90	83	86.5
2023	76	121	8	CHATO	CHULUCANAS	KENT	1	2	1	62	371	100	83	78	80.5
2023	77	121	8	CHATO	CHULUCANAS	KENT	1	2	2	62	381	95	89	81	85.0
2023	78	121	8	CHATO	CHULUCANAS	KENT	1	2	3	62	380	102	87	79	83.0
2023	79	121	8	CHATO	CHULUCANAS	KENT	1	2	4	62	452	105	91	84	87.5
2023	80	121	8	CHATO	CHULUCANAS	KENT	1	2	5	62	455	104	90	84	87.0
2023	81	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	1	51	385	98	90	79	84.5
2023	82	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	2	51	610	119	96	87	91.5
2023	83	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	3	51	640	120	100	86	93.0
2023	84	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	4	51	430	103	92	80	86.0
2023	85	141	1	CHULUCANAS	CHULUCANAS	KENT	2	1	5	51	500	107	95	87	91.0
2023	86	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	1	38	630	118	101	91	96.0
2023	87	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	2	38	512	107	97	86	91.5
2023	88	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	3	38	652	126	102	83	92.5
2023	89	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	4	38	510	108	93	88	90.5
2023	90	141	1	CHULUCANAS	CHULUCANAS	KENT	2	2	5	38	495	110	95	84	89.5
2023	91	231	2	CHATO	JULIE	KENT	2	1	1	57	441	102	93	82	87.5
2023	92	231	2	CHATO	JULIE	KENT	2	1	2	57	471	105	88	83	85.5
2023	93	231	2	CHATO	JULIE	KENT	2	1	3	57	645	120	100	95	97.5
2023	94	231	2	CHATO	JULIE	KENT	2	1	4	57	525	114	90	85	87.5
2023	95	231	2	CHATO	JULIE	KENT	2	1	5	57	360	101	89	72	80.5
2023	96	231	2	CHATO	JULIE	KENT	2	2	1	116	555	115	99	87	93.0
2023	97	231	2	CHATO	JULIE	KENT	2	2	2	116	495	116	91	80	85.5
2023	98	231	2	CHATO	JULIE	KENT	2	2	3	116	512	114	93	84	88.5
2023	99	231	2	CHATO	JULIE	KENT	2	2	4	116	605	119	95	85	90.0
2023	100	231	2	CHATO	JULIE	KENT	2	2	5	116	520	109	99	87	93.0
2023	101	241	3	CHULUCANAS	CHATO	KENT	2	1	1	91	425	102	91	82	86.5
2023	102	241	3	CHULUCANAS	CHATO	KENT	2	1	2	91	600	118	101	88	94.5
2023	103	241	3	CHULUCANAS	CHATO	KENT	2	1	3	91	765	119	107	94	100.5
2023	104	241	3	CHULUCANAS	CHATO	KENT	2	1	4	91	426	108	87	76	81.5
2023	105	241	3	CHULUCANAS	CHATO	KENT	2	1	5	91	475	106	97	84	90.5
2023	106	241	3	CHULUCANAS	CHATO	KENT	2	2	1	94	545	109	95	86	90.5
2023	107	241	3	CHULUCANAS	CHATO	KENT	2	2	2	94	371	100	85	78	81.5
2023	108	241	3	CHULUCANAS	CHATO	KENT	2	2	3	94	470	102	89	85	87.0
2023	109	241	3	CHULUCANAS	CHATO	KENT	2	2	4	94	445	105	94	83	88.5
2023	110	241	3	CHULUCANAS	CHATO	KENT	2	2	5	94	575	115	96	85	90.5
2023	111	211	4	CHATO	IRWIN	KENT	2	1	1	123	489	110	95	83	89.0
2023	112	211	4	CHATO	IRWIN	KENT	2	1	2	123	370	99	88	78	83.0
2023	113	211	4	CHATO	IRWIN	KENT	2	1	3	123	472	108	93	82	87.5
2023	114	211	4	CHATO	IRWIN	KENT	2	1	4	123	460	108	91	78	84.5
2023	115	211	4	CHATO	IRWIN	KENT	2	1	5	123	391	103	89	77	83.0
2023	116	211	4	CHATO	IRWIN	KENT	2	2	1	89	450	106	88	80	84.0
2023	117	211	4	CHATO	IRWIN	KENT	2	2	2	89	535	107	98	89	93.5
2023	118	211	4	CHATO	IRWIN	KENT	2	2	3	89	410	104	87	75	81.0
2023	119	211	4	CHATO	IRWIN	KENT	2	2	4	89	490	106	95	83	89.0
2023	120	211	4	CHATO	IRWIN	KENT	2	2	5	89	376	98	88	74	81.0
2023	121	131	5	CHULUCANAS	JULIE	KENT	2	1	1	109	385	98	89	79	84.0
2023	122	131	5	CHULUCANAS	JULIE	KENT	2	1	2	109	400	100	85	80	82.5
2023	123	131	5	CHULUCANAS	JULIE	KENT	2	1	3	109	635	120	100	88	94.0
2023	124	131	5	CHULUCANAS	JULIE	KENT	2	1	4	109	616	123	93	81	87.0
2023	125	131	5	CHULUCANAS	JULIE	KENT	2	1	5	109	445	108	89	78	83.5
2023	126	131	5	CHULUCANAS	JULIE	KENT	2	2	1	63	585	120	96	81	88.5
2023	127	131	5	CHULUCANAS	JULIE	KENT	2	2	2	63	388	99	88	81	84.5
2023	128	131	5	CHULUCANAS	JULIE	KENT	2	2	3	63	717	129	106	87	96.5
2023	129	131	5	CHULUCANAS	JULIE	KENT	2	2	4	63	478	107	95	83	89.0
2023	130	131	5	CHULUCANAS	JULIE	KENT	2	2	5	63	445	108	89	78	83.5
2023	131	111	6	CHULUCANAS	IRWIN	KENT	2	1	1	126	460	101	94	83	88.5
2023	132	111	6	CHULUCANAS	IRWIN	KENT	2	1	2	126	440	99	89	86	87.5
2023	133	111	6	CHULUCANAS	IRWIN	KENT	2	1	3	126	535	112	96	86	91.0
2023	134	111	6	CHULUCANAS	IRWIN	KENT	2	1	4	126	560	118	95	87	91.0
2023	135	111	6	CHULUCANAS	IRWIN	KENT	2	1	5	126	420	106	84	78	81.0
2023	136	111	6	CHULUCANAS	IRWIN	KENT	2	2	1	56	380	100	84	77	80.5
2023	137	111	6	CHULUCANAS	IRWIN	KENT	2	2	2	56	368	98	85	75	80.0
2023	138	111	6	CHULUCANAS	IRWIN	KENT	2	2	3	56	500	114	92	84	88.0
2023	139	111	6	CHULUCANAS	IRWIN	KENT	2	2	4	56	310	92	80	75	77.5
2023	140	111	6	CHULUCANAS	IRWIN	KENT	2	2	5	56	400	100	84	79	81.5
2023	141	221	7	CHATO	CHATO	KENT	2	1	1	103	405	100	89	79	84.0
2023	142	221	7	CHATO	CHATO	KENT	2	1	2	103	460	107	90	83	86.5
2023	143	221	7	CHATO	CHATO	KENT	2	1	3	103	470	107	90	81	85.5
2023	144	221	7	CHATO	CHATO	KENT	2	1	4	103	410	105	88	74	81.0
2023	145	221	7	CHATO	CHATO	KENT	2	1	5	103	610	115	103	92	97.5
2023	146	221	7	CHATO	CHATO	KENT	2	2	1	124	470	111	92	79	85.5
2023	147	221	7	CHATO	CHATO	KENT	2	2	2	124	435	109	86	79	82.5
2023	148	221	7	CHATO	CHATO	KENT	2	2	3	124	445	107	85	80	82.5
2023	149	221	7	CHATO	CHATO	KENT	2	2	4	124	430	104	91	79	85.0
2023	150	221	7	CHATO	CHATO	KENT	2	2	5	124	639	115	99	90	94.5
2023	151	121	8	CHATO	CHULUCANAS	KENT	2	1	1	95	615	119	98	89	93.5
2023	152	121	8	CHATO	CHULUCANAS	KENT	2	1	2	95	430	103	92	82	87.0
2023	153	121	8	CHATO	CHULUCANAS	KENT	2	1	3	95	460	104	89	84	86.5
2023	154	121	8	CHATO	CHULUCANAS	KENT	2	1	4	95	391	94	92	82	87.0
2023	155	121	8	CHATO	CHULUCANAS	KENT	2	1	5	95	410	100	93	80	86.5
2023	156	121	8	CHATO	CHULUCANAS	KENT	2	2	1	79	489	109	87	83	85.0
2023	157	121	8	CHATO	CHULUCANAS	KENT	2	2	2	79	569	113	98	89	93.5
2023	158	121	8	CHATO	CHULUCANAS	KENT	2	2	3	79	566	116	95	84	89.5
2023	159	121	8	CHATO	CHULUCANAS	KENT	2	2	4	79	475	106	96	84	90.0
2023	160	121	8	CHATO	CHULUCANAS	KENT	2	2	5	79	490	110	91	82	86.5
2023	161	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	1	85	361	95	86	78	82.0
2023	162	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	2	85	460	105	93	81	87.0
2023	163	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	3	85	465	103	94	82	88.0
2023	164	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	4	85	450	108	84	80	82.0
2023	165	141	1	CHULUCANAS	CHULUCANAS	KENT	3	1	5	85	552	111	97	84	90.5
2023	166	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	1	113	351	96	84	77	80.5
2023	167	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	2	113	584	117	97	84	90.5
2023	168	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	3	113	440	109	89	77	83.0
2023	169	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	4	113	420	103	90	80	85.0
2023	170	141	1	CHULUCANAS	CHULUCANAS	KENT	3	2	5	113	361	100	86	72	79.0
2023	171	231	2	CHATO	JULIE	KENT	3	1	1	57	645	120	102	89	95.5
2023	172	231	2	CHATO	JULIE	KENT	3	1	2	57	440	105	83	79	81.0
2023	173	231	2	CHATO	JULIE	KENT	3	1	3	57	300	97	81	70	75.5
2023	174	231	2	CHATO	JULIE	KENT	3	1	4	57	495	113	96	81	88.5
2023	175	231	2	CHATO	JULIE	KENT	3	1	5	57	460	111	88	78	83.0
2023	176	231	2	CHATO	JULIE	KENT	3	2	1	53	360	102	89	72	80.5
2023	177	231	2	CHATO	JULIE	KENT	3	2	2	53	444	108	86	82	84.0
2023	178	231	2	CHATO	JULIE	KENT	3	2	3	53	489	112	91	83	87.0
2023	179	231	2	CHATO	JULIE	KENT	3	2	4	53	488	111	93	82	87.5
2023	180	231	2	CHATO	JULIE	KENT	3	2	5	53	400	101	87	79	83.0
2023	181	241	3	CHULUCANAS	CHATO	KENT	3	1	1	87	655	123	101	91	96.0
2023	182	241	3	CHULUCANAS	CHATO	KENT	3	1	2	87	392	103	88	78	83.0
2023	183	241	3	CHULUCANAS	CHATO	KENT	3	1	3	87	316	96	83	73	78.0
2023	184	241	3	CHULUCANAS	CHATO	KENT	3	1	4	87	420	101	86	83	84.5
2023	185	241	3	CHULUCANAS	CHATO	KENT	3	1	5	87	410	103	87	79	83.0
2023	186	241	3	CHULUCANAS	CHATO	KENT	3	2	1	64	490	108	88	82	85.0
2023	187	241	3	CHULUCANAS	CHATO	KENT	3	2	2	64	400	95	89	82	85.5
2023	188	241	3	CHULUCANAS	CHATO	KENT	3	2	3	64	575	112	96	89	92.5
2023	189	241	3	CHULUCANAS	CHATO	KENT	3	2	4	64	613	112	98	91	94.5
2023	190	241	3	CHULUCANAS	CHATO	KENT	3	2	5	64	410	103	88	80	84.0
2023	191	211	4	CHATO	IRWIN	KENT	3	1	1	92	530	113	99	88	93.5
2023	192	211	4	CHATO	IRWIN	KENT	3	1	2	92	391	101	90	77	83.5
2023	193	211	4	CHATO	IRWIN	KENT	3	1	3	92	418	102	89	77	83.0
2023	194	211	4	CHATO	IRWIN	KENT	3	1	4	92	450	100	87	83	85.0
2023	195	211	4	CHATO	IRWIN	KENT	3	1	5	92	550	112	97	85	91.0
2023	196	211	4	CHATO	IRWIN	KENT	3	2	1	96	510	112	93	80	86.5
2023	197	211	4	CHATO	IRWIN	KENT	3	2	2	96	700	124	102	90	96.0
2023	198	211	4	CHATO	IRWIN	KENT	3	2	3	96	470	104	89	87	88.0
2023	199	211	4	CHATO	IRWIN	KENT	3	2	4	96	350	95	82	77	79.5
2023	200	211	4	CHATO	IRWIN	KENT	3	2	5	96	415	101	89	80	84.5
2023	201	131	5	CHULUCANAS	JULIE	KENT	3	1	1	64	450	108	91	78	84.5
2023	202	131	5	CHULUCANAS	JULIE	KENT	3	1	2	64	420	109	90	75	82.5
2023	203	131	5	CHULUCANAS	JULIE	KENT	3	1	3	64	640	120	104	90	97.0
2023	204	131	5	CHULUCANAS	JULIE	KENT	3	1	4	64	520	111	91	83	87.0
2023	205	131	5	CHULUCANAS	JULIE	KENT	3	1	5	64	625	126	95	86	90.5
2023	206	131	5	CHULUCANAS	JULIE	KENT	3	2	1	162	490	110	89	82	85.5
2023	207	131	5	CHULUCANAS	JULIE	KENT	3	2	2	162	515	107	92	84	88.0
2023	208	131	5	CHULUCANAS	JULIE	KENT	3	2	3	162	390	103	85	75	80.0
2023	209	131	5	CHULUCANAS	JULIE	KENT	3	2	4	162	465	105	89	80	84.5
2023	210	131	5	CHULUCANAS	JULIE	KENT	3	2	5	162	615	118	100	89	94.5
2023	211	111	6	CHULUCANAS	IRWIN	KENT	3	1	1	128	465	105	90	80	85.0
2023	212	111	6	CHULUCANAS	IRWIN	KENT	3	1	2	128	535	116	93	85	89.0
2023	213	111	6	CHULUCANAS	IRWIN	KENT	3	1	3	128	375	100	85	76	80.5
2023	214	111	6	CHULUCANAS	IRWIN	KENT	3	1	4	128	410	103	86	77	81.5
2023	215	111	6	CHULUCANAS	IRWIN	KENT	3	1	5	128	435	109	87	81	84.0
2023	216	111	6	CHULUCANAS	IRWIN	KENT	3	2	1	93	452	108	88	81	84.5
2023	217	111	6	CHULUCANAS	IRWIN	KENT	3	2	2	93	540	117	92	84	88.0
2023	218	111	6	CHULUCANAS	IRWIN	KENT	3	2	3	93	426	105	85	79	82.0
2023	219	111	6	CHULUCANAS	IRWIN	KENT	3	2	4	93	366	106	84	75	79.5
2023	220	111	6	CHULUCANAS	IRWIN	KENT	3	2	5	93	435	108	93	78	85.5
2023	221	221	7	CHATO	CHATO	KENT	3	1	1	113	615	120	101	85	93.0
2023	222	221	7	CHATO	CHATO	KENT	3	1	2	113	505	113	90	80	85.0
2023	223	221	7	CHATO	CHATO	KENT	3	1	3	113	430	107	86	78	82.0
2023	224	221	7	CHATO	CHATO	KENT	3	1	4	113	475	106	92	83	87.5
2023	225	221	7	CHATO	CHATO	KENT	3	1	5	113	325	93	81	72	76.5
2023	226	221	7	CHATO	CHATO	KENT	3	2	1	99	470	106	89	82	85.5
2023	227	221	7	CHATO	CHATO	KENT	3	2	2	99	480	109	96	80	88.0
2023	228	221	7	CHATO	CHATO	KENT	3	2	3	99	525	109	97	85	91.0
2023	229	221	7	CHATO	CHATO	KENT	3	2	4	99	452	113	94	77	85.5
2023	230	221	7	CHATO	CHATO	KENT	3	2	5	99	416	101	89	82	85.5
2023	231	121	8	CHATO	CHULUCANAS	KENT	3	1	1	103	470	108	90	81	85.5
2023	232	121	8	CHATO	CHULUCANAS	KENT	3	1	2	103	410	105	88	76	82.0
2023	233	121	8	CHATO	CHULUCANAS	KENT	3	1	3	103	450	111	89	81	85.0
2023	234	121	8	CHATO	CHULUCANAS	KENT	3	1	4	103	620	119	100	86	93.0
2023	235	121	8	CHATO	CHULUCANAS	KENT	3	1	5	103
2023	236	121	8	CHATO	CHULUCANAS	KENT	3	2	1	108	471	111	90	80	85.0
2023	237	121	8	CHATO	CHULUCANAS	KENT	3	2	2	108	475	110	93	81	87.0
2023	238	121	8	CHATO	CHULUCANAS	KENT	3	2	3	108	525	114	91	81	86.0
2023	239	121	8	CHATO	CHULUCANAS	KENT	3	2	4	108	426	100	86	82	84.0
2023	240	121	8	CHATO	CHULUCANAS	KENT	3	2	5	108	527	113	88	85	86.5

3 Data summary

Summary of the number of data points recorded for each treatment and evaluated variable.

sm <- rdt %>% 
  group_by(year, treat) %>% 
  summarise(across(height:sproud, ~ sum(!is.na(.))))

sm
## # A tibble: 24 × 6
## # Groups:   year [3]
##    year  treat height n_fruits flowering sproud
##    <fct> <fct>  <int>    <int>     <int>  <int>
##  1 2017  111        0       26        26      0
##  2 2017  121        0       26        26      0
##  3 2017  131        0       25        25      0
##  4 2017  141        0       26        26      0
##  5 2017  211        0       26        26      0
##  6 2017  221        0       26        26      0
##  7 2017  231        0       26        26      0
##  8 2017  241        0       27        27      0
##  9 2018  111        0        9        22      0
## 10 2018  121        0        9        23      0
## # ℹ 14 more rows

sm <- fru %>% 
  group_by(year, treat) %>% 
  summarise(across(weigth:diameter_average, ~ sum(!is.na(.))))

sm
## # A tibble: 8 × 7
## # Groups:   year [1]
##   year  treat weigth  long diameter_1 diameter_2 diameter_average
##   <fct> <fct>  <int> <int>      <int>      <int>            <int>
## 1 2023  111       30    30         30         30               30
## 2 2023  121       29    29         29         29               29
## 3 2023  131       30    30         30         30               30
## 4 2023  141       30    30         30         30               30
## 5 2023  211       30    30         30         30               30
## 6 2023  221       30    30         30         30               30
## 7 2023  231       30    30         30         30               30
## 8 2023  241       30    30         30         30               30

4 Meteorological data

Climatic conditions of the study area located in the Tambogrande district, Piura region.

met <- range_read(ss = gs, sheet = "clima") %>% 
  mutate(date = as_date(Fecha))

scale <- 2

plot <- met %>% 
  ggplot(aes(x = date)) +
  geom_line(aes(y = TMax, color = "Tmax (°C)"), size= 0.8, linetype = "longdash") + 
  geom_line(aes(y = TMin, color = "Tmin (°C)"), size= 0.8, linetype = "dotted") +
  geom_bar(aes(y = PP/scale)
            , stat="identity", size=.1, fill="blue", color="black", alpha=.4) +
  geom_line(aes(y = HR/scale, color = "HR (%)"), size = 0.8, linetype = "twodash") +
  scale_color_manual("", values = c("skyblue", "red", "blue")) +
  scale_y_continuous(limits = c(0, 50)
                     , expand = c(0, 0)
                     , name = "Temperature (°C)"
                     , sec.axis = sec_axis(~ . * scale, name = "Precipitation (mm)")
                     ) +
  scale_x_date(date_breaks = "3 month", date_labels = "%b-%Y", name = NULL) +
  theme_minimal_grid() +
  theme(legend.position = "top") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1))

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.jpg", units = "cm"
         , width = 25, height = 15)

plot %>% 
  ggsave2(plot = ., "submission/Figure_2.eps", units = "cm"
         , width = 25, height = 15)

knitr::include_graphics("submission/Figure_2.jpg")

5 Objetives

Evaluate the effect of the rootstock-interstock interaction on the agronomic traits and fruit biometrics of the mango crop in the San Lorenzo Valley.

5.1 Specific Objective 1

Determine the effect of the rootstock-interstock interaction on the agronomic characteristics of mango.

5.1.1 Plant height

trait <- "height"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block      stock  edge height      resi   res_MAD   rawp.BHStud
## 87    523     2      CHATO JULIE   2.00 -1.643199 -3.984232 0.00006769863
## 126   563     2 CHULUCANAS CHATO   1.90 -1.731293 -4.197830 0.00002694848
## 127   564     2 CHULUCANAS CHATO   1.85 -1.781293 -4.319064 0.00001566924
##              adjp       bholm out_flag
## 87  0.00006769863 0.013810521  OUTLIER
## 126 0.00002694848 0.005524438  OUTLIER
## 127 0.00001566924 0.003227863  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: height
##             Df Sum Sq Mean Sq F value   Pr(>F)    
## block        2  3.140 1.57012  9.1832 0.000155 ***
## stock        1  1.273 1.27268  7.4435 0.006954 ** 
## edge         3  1.994 0.66450  3.8865 0.009977 ** 
## stock:edge   3  2.301 0.76689  4.4853 0.004549 ** 
## Residuals  193 32.999 0.17098                     
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ stock|edge) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()

stock	edge	emmean	SE	df	lower.CL	upper.CL	group
CHATO	CHATO	3.741984	0.0811085	193	3.582011	3.901957	Aa
CHATO	CHULUCANAS	3.662963	0.0795773	193	3.506010	3.819916	Aa
CHATO	IRWIN	3.915061	0.0811085	193	3.755088	4.075034	Aa
CHATO	JULIE	3.860337	0.0827154	193	3.697194	4.023479	Aa
CHULUCANAS	CHATO	3.925048	0.0827647	193	3.761808	4.088287	Aa
CHULUCANAS	CHULUCANAS	3.497320	0.0811085	193	3.337347	3.657293	Ab
CHULUCANAS	IRWIN	3.649114	0.0844563	193	3.482538	3.815690	Bab
CHULUCANAS	JULIE	3.467553	0.0844563	193	3.300977	3.634129	Bb

p1a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Plant height (m)"
           , glab = "Interstock"
           , ylimits = c(0, 6, 2)
           , 
           )

p1a

5.1.2 Sproud

trait <- "sproud"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block      stock       edge sproud      resi   res_MAD  rawp.BHStud
## 8     441     1 CHULUCANAS CHULUCANAS      0 -63.26923 -4.267451 0.0000197719
## 126   567     2 CHULUCANAS      CHATO     10 -58.26923 -3.930206 0.0000848732
##             adjp       bholm out_flag
## 8   0.0000197719 0.003974153  OUTLIER
## 126 0.0000848732 0.016974639  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: sproud
##             Df Sum Sq Mean Sq F value     Pr(>F)    
## block        2    362   180.9  0.5152    0.59820    
## stock        1   6579  6578.9 18.7416 0.00002425 ***
## edge         3   2054   684.7  1.9506    0.12292    
## stock:edge   3   2713   904.2  2.5758    0.05521 .  
## Residuals  189  66345   351.0                       
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ stock|edge) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()

stock	edge	emmean	SE	df	lower.CL	upper.CL	group
CHATO	CHATO	54.81356	3.747943	189	47.42039	62.20674	Ba
CHATO	CHULUCANAS	65.17927	3.675132	189	57.92972	72.42882	Aa
CHATO	IRWIN	52.08471	3.750192	189	44.68710	59.48232	Aa
CHATO	JULIE	51.91004	3.675132	189	44.66049	59.15959	Ba
CHULUCANAS	CHATO	70.57288	3.750234	189	63.17519	77.97057	Aa
CHULUCANAS	CHULUCANAS	65.94241	3.750188	189	58.54481	73.34001	Aa
CHULUCANAS	IRWIN	61.21227	3.910119	189	53.49919	68.92535	Aa
CHULUCANAS	JULIE	72.15745	3.826839	189	64.60864	79.70625	Aa

p1b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Sproud ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           )

p1b 

5.1.3 Number of fruits

trait <- "n_fruits"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block year stock  edge treat n_fruits     resi  res_MAD   rawp.BHStud
## 442   604     3 2019 CHATO JULIE   231      539 287.2486 4.008689 0.00006105685
##              adjp      bholm out_flag
## 442 0.00006105685 0.02955151  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: n_fruits
##             Df  Sum Sq Mean Sq F value          Pr(>F)    
## block        2  261207  130604 21.6839 0.0000000009805 ***
## year         2  126248   63124 10.4804 0.0000352211768 ***
## stock        1    9326    9326  1.5483          0.2140    
## edge         3   22131    7377  1.2248          0.3001    
## stock:edge   3   15548    5183  0.8605          0.4615    
## Residuals  471 2836870    6023                            
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock|year) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()

year	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
2017	CHATO	CHATO	162.5132	10.74838	471	141.3925	183.6339	Ba
2017	CHATO	CHULUCANAS	170.3860	10.59352	471	149.5696	191.2024	Ba
2017	CHULUCANAS	CHATO	171.7745	10.74778	471	150.6550	192.8940	Ba
2017	CHULUCANAS	CHULUCANAS	198.8729	10.74778	471	177.7533	219.9924	Ba
2017	IRWIN	CHATO	178.3001	10.74838	471	157.1794	199.4208	Ba
2017	IRWIN	CHULUCANAS	174.7851	10.92618	471	153.3150	196.2552	Ba
2017	JULIE	CHATO	173.0122	10.86339	471	151.6655	194.3589	Ba
2017	JULIE	CHULUCANAS	176.5933	10.91078	471	155.1535	198.0331	Ba
2018	CHATO	CHATO	197.2613	13.04649	471	171.6248	222.8978	Aa
2018	CHATO	CHULUCANAS	205.1341	12.95537	471	179.6767	230.5916	Aa
2018	CHULUCANAS	CHATO	206.5226	13.04648	471	180.8861	232.1591	Aa
2018	CHULUCANAS	CHULUCANAS	233.6210	13.04648	471	207.9845	259.2575	Aa
2018	IRWIN	CHATO	213.0482	13.04649	471	187.4117	238.6847	Aa
2018	IRWIN	CHULUCANAS	209.5332	13.19501	471	183.6048	235.4616	Aa
2018	JULIE	CHATO	207.7604	13.14195	471	181.9362	233.5845	Aa
2018	JULIE	CHULUCANAS	211.3414	13.14337	471	185.5145	237.1683	Aa
2019	CHATO	CHATO	194.2890	10.75243	471	173.1603	215.4177	Aa
2019	CHATO	CHULUCANAS	202.1618	10.59847	471	181.3357	222.9880	Aa
2019	CHULUCANAS	CHATO	203.5503	10.75303	471	182.4205	224.6802	Aa
2019	CHULUCANAS	CHULUCANAS	230.6487	10.75303	471	209.5188	251.7785	Aa
2019	IRWIN	CHATO	210.0759	10.75243	471	188.9472	231.2046	Aa
2019	IRWIN	CHULUCANAS	206.5609	11.07069	471	184.8068	228.3150	Aa
2019	JULIE	CHATO	204.7881	10.95974	471	183.2520	226.3241	Aa
2019	JULIE	CHULUCANAS	208.3691	10.91480	471	186.9214	229.8169	Aa

p1c <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Fruits number"
           , glab = "Interstock"
           , ylimits = c(0, 320, 60)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1c 

5.1.4 Flowering

trait <- "flowering"

lmm <- paste({{trait}}, "~ 1 + (1|block) + year + stock*edge + (1 + year|treat)") %>% as.formula()

lmd <- paste({{trait}}, "~ block + year + stock*edge") %>% as.formula()

rmout <- rdt %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##     index block year      stock       edge treat flowering      resi   res_MAD
## 28     31     1 2017      CHATO      IRWIN   211         5 -68.74456 -6.212394
## 32     35     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 33     36     1 2017      CHATO      IRWIN   211        25 -48.74456 -4.405009
## 64     67     1 2017      CHATO      CHATO   221        20 -56.92226 -5.144022
## 65     68     1 2017      CHATO      CHATO   221        30 -46.92226 -4.240330
## 67     70     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 69     72     1 2017      CHATO      CHATO   221        15 -61.92226 -5.595869
## 127   131     2 2017 CHULUCANAS      CHATO   241        25 -58.44225 -5.281382
## 166   171     3 2017 CHULUCANAS CHULUCANAS   141        20 -58.28192 -5.266893
## 213   225     1 2018 CHULUCANAS CHULUCANAS   141        20 -57.68630 -5.213068
## 232   258     1 2018 CHULUCANAS      JULIE   131        10 -56.04249 -5.064518
## 243   273     1 2018 CHULUCANAS      IRWIN   111        15 -58.16121 -5.255986
## 250   284     1 2018      CHATO      CHATO   221        90  47.03144  4.250196
## 251   288     1 2018      CHATO      CHATO   221        97  54.03144  4.882781
## 254   292     2 2018      CHATO      IRWIN   211         5 -63.87075 -5.771952
## 289   328     2 2018      CHATO      CHATO   221         3 -43.83305 -3.961160
## 292   331     2 2018      CHATO      CHATO   221         0 -46.83305 -4.232268
## 298   338     2 2018 CHULUCANAS      IRWIN   111        10 -67.02571 -6.057063
## 299   339     2 2018 CHULUCANAS      IRWIN   111         5 -72.02571 -6.508909
## 311   352     2 2018 CHULUCANAS      JULIE   131        20 -49.90698 -4.510056
## 333   376     3 2018      CHATO      IRWIN   211        20 -44.36027 -4.008804
## 342   385     3 2018 CHULUCANAS CHULUCANAS   141         0 -77.04031 -6.962075
## 346   389     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 347   390     3 2018      CHATO      JULIE   231         0 -47.63760 -4.304974
## 349   392     3 2018      CHATO      JULIE   231         2 -45.63760 -4.124236
## 375   421     3 2018 CHULUCANAS      JULIE   131        10 -55.39650 -5.006140
## 376   422     3 2018 CHULUCANAS      JULIE   131        20 -45.39650 -4.102448
## 454   505     2 2019      CHATO      IRWIN   211        30 -48.76953 -4.407266
## 526   581     3 2019      CHATO CHULUCANAS   121         7 -70.54614 -6.375202
##              rawp.BHStud                 adjp             bholm out_flag
## 28  0.000000000521833021 0.000000000521833021 0.000000305794150  OUTLIER
## 32  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 33  0.000010577939756562 0.000010577939756562 0.006040003600997  OUTLIER
## 64  0.000000268917649482 0.000000268917649482 0.000155165483751  OUTLIER
## 65  0.000022319169655338 0.000022319169655338 0.012632650024921  OUTLIER
## 67  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 69  0.000000021952000884 0.000000021952000884 0.000012798016515  OUTLIER
## 127 0.000000128212855666 0.000000128212855666 0.000074491669142  OUTLIER
## 166 0.000000138751758749 0.000000138751758749 0.000080476020075  OUTLIER
## 213 0.000000185743196379 0.000000185743196379 0.000107359567507  OUTLIER
## 232 0.000000409435427784 0.000000409435427784 0.000235834806404  OUTLIER
## 243 0.000000147233846226 0.000000147233846226 0.000085248396965  OUTLIER
## 250 0.000021358339519217 0.000021358339519217 0.012110178507396  OUTLIER
## 251 0.000001046000612570 0.000001046000612570 0.000600404351615  OUTLIER
## 254 0.000000007835861071 0.000000007835861071 0.000004576142866  OUTLIER
## 289 0.000074586480278027 0.000074586480278027 0.041843015435973  OUTLIER
## 292 0.000023134658141855 0.000023134658141855 0.013071081850148  OUTLIER
## 298 0.000000001386292858 0.000000001386292858 0.000000810981322  OUTLIER
## 299 0.000000000075698336 0.000000000075698336 0.000000044510622  OUTLIER
## 311 0.000006481038199890 0.000006481038199890 0.003713634888537  OUTLIER
## 333 0.000061026989556012 0.000061026989556012 0.034297168130479  OUTLIER
## 342 0.000000000003352874 0.000000000003352874 0.000000001974843  OUTLIER
## 346 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 347 0.000016700498484123 0.000016700498484123 0.009502583637466  OUTLIER
## 349 0.000037196739853362 0.000037196739853362 0.020978961277296  OUTLIER
## 375 0.000000555322711371 0.000000555322711371 0.000319310559038  OUTLIER
## 376 0.000040880207851313 0.000040880207851313 0.023015557020289  OUTLIER
## 454 0.000010468378508710 0.000010468378508710 0.005987912506982  OUTLIER
## 526 0.000000000182721394 0.000000000182721394 0.000000107257458  OUTLIER

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: flowering
##             Df Sum Sq Mean Sq F value            Pr(>F)    
## block        2   1978   988.9  3.3541           0.03566 *  
## year         2  16473  8236.6 27.9371 0.000000000002796 ***
## stock        1    804   803.6  2.7257           0.09932 .  
## edge         3   7276  2425.3  8.2263 0.000023116374039 ***
## stock:edge   3   1580   526.5  1.7859           0.14874    
## Residuals  548 161564   294.8                              
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc1 <- emmeans(model, ~ edge|stock|year) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  rename(sig1 = ".group")

mc2 <- emmeans(model, ~ year|edge|stock) %>%
  cld(Letters = letters, reversed = T) %>% 
  mutate(across(".group", ~ trimws(.))) %>% 
  mutate(across(".group", ~ toupper(.))) %>% 
  rename(sig2 = ".group")

mc <- merge(mc2, mc1) %>% 
  unite(col = "group", c("sig2", "sig1"), sep = "")

mc %>% kable()

year	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
2017	CHATO	CHATO	76.99530	2.345782	548	72.38748	81.60313	Ab
2017	CHATO	CHULUCANAS	77.04841	2.208166	548	72.71090	81.38591	Ab
2017	CHULUCANAS	CHATO	87.25102	2.226577	548	82.87735	91.62469	Aa
2017	CHULUCANAS	CHULUCANAS	85.96947	2.254128	548	81.54168	90.39726	Aa
2017	IRWIN	CHATO	80.99148	2.311773	548	76.45046	85.53250	Aab
2017	IRWIN	CHULUCANAS	86.68903	2.284768	548	82.20105	91.17700	Aa
2017	JULIE	CHATO	78.20890	2.263368	548	73.76296	82.65484	Ab
2017	JULIE	CHULUCANAS	84.54702	2.315901	548	79.99789	89.09615	Aa
2018	CHATO	CHATO	64.90407	2.425731	548	60.13920	69.66894	Bb
2018	CHATO	CHULUCANAS	64.95718	2.269647	548	60.49890	69.41545	Bb
2018	CHULUCANAS	CHATO	75.15979	2.289284	548	70.66294	79.65663	Ba
2018	CHULUCANAS	CHULUCANAS	73.87824	2.329225	548	69.30293	78.45354	Ba
2018	IRWIN	CHATO	68.90025	2.375187	548	64.23467	73.56584	Bab
2018	IRWIN	CHULUCANAS	74.59780	2.389683	548	69.90374	79.29186	Ba
2018	JULIE	CHATO	66.11767	2.367724	548	61.46675	70.76860	Bb
2018	JULIE	CHULUCANAS	72.45579	2.429118	548	67.68427	77.22731	Ba
2019	CHATO	CHATO	77.01196	2.305001	548	72.48424	81.53968	Ab
2019	CHATO	CHULUCANAS	77.06506	2.198633	548	72.74628	81.38384	Ab
2019	CHULUCANAS	CHATO	87.26768	2.233767	548	82.87988	91.65547	Aa
2019	CHULUCANAS	CHULUCANAS	85.98612	2.243058	548	81.58008	90.39217	Aa
2019	IRWIN	CHATO	81.00814	2.291841	548	76.50627	85.51001	Aab
2019	IRWIN	CHULUCANAS	86.70568	2.312242	548	82.16374	91.24763	Aa
2019	JULIE	CHATO	78.22556	2.271401	548	73.76384	82.68728	Ab
2019	JULIE	CHULUCANAS	84.56367	2.325199	548	79.99628	89.13107	Aa

p1d <- mc %>% 
  plot_smr(type = "bar"
           , x = "year"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Year"
           , ylab = "Flowering ('%')"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           ) +
  facet_wrap(. ~ stock, nrow = 2)

p1d

5.1.5 Figure 3

Univariate analysis of the variables that determine the agronomic characteristics of mango.

legend <- cowplot::get_plot_component(p1a, 'guide-box-top', return_all = TRUE)

p1 <- list(p1a + theme(legend.position="none")
           , p1b + theme(legend.position="none")
           , p1c + theme(legend.position="none")
           , p1d + theme(legend.position="none")
           ) %>% 
  plot_grid(plotlist = ., ncol = 2
            , labels = "auto"
            , rel_heights = c(1, 2)
            ) 

fig <- plot_grid(legend, p1, ncol = 1, align = 'v', rel_heights = c(0.05, 1))

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.jpg"
         , units = "cm"
         , width = 24
         , height = 16
         )

fig %>% 
  ggsave2(plot = ., "submission/Figure_3.eps"
         , units = "cm"
         , width = 24
         , height = 16
         )

knitr::include_graphics("submission/Figure_3.jpg")

5.1.6 Multivariate

Principal Component Analysis (PCA) of agronomic traits in the mango crop based on the use of rootstock-interstock combinations.

mv <- rdt %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%   
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Height = height
         , Fruits = n_fruits
         , Flowering = flowering
         , Sproud = sproud)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3  Dim.4
## Variance               2.49   0.93   0.37   0.21
## % of var.             62.27  23.26   9.22   5.26
## Cumulative % of var.  62.27  85.52  94.74 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  2.28 | -1.99 19.84  0.76 | -0.72  7.04  0.10 | -0.43
## 2                     |  1.67 |  1.05  5.51  0.39 | -0.61  4.97  0.13 |  1.15
## 3                     |  2.18 | -1.90 18.19  0.76 |  0.89 10.71  0.17 |  0.36
## 4                     |  1.74 | -1.44 10.38  0.68 |  0.94 11.90  0.29 | -0.19
## 5                     |  1.52 | -0.49  1.23  0.11 | -1.05 14.72  0.47 | -0.41
## 6                     |  2.83 |  2.26 25.69  0.64 |  1.63 35.67  0.33 | -0.51
## 7                     |  1.00 |  0.68  2.34  0.46 | -0.03  0.01  0.00 |  0.67
## 8                     |  2.21 |  1.83 16.82  0.69 | -1.06 14.98  0.23 | -0.63
##                         ctr  cos2  
## 1                      6.36  0.04 |
## 2                     44.91  0.47 |
## 3                      4.40  0.03 |
## 4                      1.21  0.01 |
## 5                      5.78  0.07 |
## 6                      8.90  0.03 |
## 7                     15.00  0.44 |
## 8                     13.43  0.08 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Height                | -0.91 33.26  0.83 |  0.07  0.53  0.00 |  0.25 17.31
## Fruits                |  0.57 13.26  0.33 |  0.78 66.19  0.62 | -0.16  6.81
## Flowering             |  0.86 29.71  0.74 |  0.05  0.23  0.00 |  0.51 69.79
## Sproud                |  0.77 23.77  0.59 | -0.55 33.05  0.31 | -0.15  6.10
##                        cos2  
## Height                 0.06 |
## Fruits                 0.03 |
## Flowering              0.26 |
## Sproud                 0.02 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  2.28 | -1.99  0.76  -1.26 | -0.72  0.10  -0.75 | -0.43
## CHATO-CHULUCANAS      |  1.67 |  1.05  0.39   0.66 | -0.61  0.13  -0.63 |  1.15
## CHATO-IRWIN           |  2.18 | -1.90  0.76  -1.21 |  0.89  0.17   0.93 |  0.36
## CHATO-JULIE           |  1.74 | -1.44  0.68  -0.91 |  0.94  0.29   0.98 | -0.19
## CHULUCANAS-CHATO      |  1.52 | -0.49  0.11  -0.31 | -1.05  0.47  -1.09 | -0.41
## CHULUCANAS-CHULUCANAS |  2.83 |  2.26  0.64   1.43 |  1.63  0.33   1.69 | -0.51
## CHULUCANAS-IRWIN      |  1.00 |  0.68  0.46   0.43 | -0.03  0.00  -0.03 |  0.67
## CHULUCANAS-JULIE      |  2.21 |  1.83  0.69   1.16 | -1.06  0.23  -1.09 | -0.63
##                        cos2 v.test  
## CHATO-CHATO            0.04  -0.71 |
## CHATO-CHULUCANAS       0.47   1.90 |
## CHATO-IRWIN            0.03   0.59 |
## CHATO-JULIE            0.01  -0.31 |
## CHULUCANAS-CHATO       0.07  -0.68 |
## CHULUCANAS-CHULUCANAS  0.03  -0.84 |
## CHULUCANAS-IRWIN       0.44   1.10 |
## CHULUCANAS-JULIE       0.08  -1.04 |

f4a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                , label = "var"
                )

f4b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                ) 

5.1.7 Figure 4

Principal Component Analysis (PCA). Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="red") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 50) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 80) + 
  labs(title = "Dim 2 - variables contribution") 

png("corrplot_output.png", width = 800, height = 800, res = 150)  
corrplot(var$cor, 
         method = "number",
         tl.col = "black", 
         tl.srt = 45)
dev.off()
## png 
##   2

img_corrplot <- ggdraw() + draw_image("corrplot_output.png")

pt4 <- img_corrplot

# Construcción del grid

fila1 <- plot_grid(f4a, f4b, ncol = 2, labels = "auto", rel_widths = c(1, 1.5))

columna_c <- plot_grid(pt1, pt2, pt3, ncol = 1, labels = "c")

fila2 <- plot_grid(columna_c, pt4, ncol = 2, labels = c("", "d"), rel_widths = c(1, 1))

grid_final <- plot_grid(fila1, fila2, ncol = 1, rel_heights = c(1, 1.2))

grid_final

ggsave2(plot = grid_final, "submission/Figure_4.jpg", height = 30, width = 28, units = "cm")

ggsave2(plot = grid_final, "submission/Figure_4.eps", height = 30, width = 28, units = "cm")

knitr::include_graphics("submission/Figure_4.jpg")

5.2 Specific Objective 2

Determine the effect of the rootstock-interstock interaction on the fruit biometrics of mango.

5.2.1 Fruit Weigth

trait <- "weigth"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##  [1] index       block       stock       edge        weigth      resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: weigth
##             Df  Sum Sq Mean Sq F value  Pr(>F)  
## block        2   73498   36749  4.6207 0.01078 *
## stock        1    1816    1816  0.2284 0.63320  
## edge         3    4318    1439  0.1810 0.90923  
## stock:edge   3   37870   12623  1.5872 0.19323  
## Residuals  229 1821257    7953                  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
1	IRWIN	CHATO	482.0333	16.28198	229	449.9517	514.1150	a
2	JULIE	CHATO	465.9667	16.28198	229	433.8850	498.0483	a
4	CHULUCANAS	CHATO	462.3661	16.56272	229	429.7313	495.0009	a
3	CHATO	CHATO	452.5333	16.28198	229	420.4517	484.6150	a
7	CHATO	CHULUCANAS	484.9667	16.28198	229	452.8850	517.0483	a
6	JULIE	CHULUCANAS	484.7000	16.28198	229	452.6184	516.7816	a
8	CHULUCANAS	CHULUCANAS	468.1667	16.28198	229	436.0850	500.2483	a
5	IRWIN	CHULUCANAS	447.2333	16.28198	229	415.1517	479.3150	a

p2a <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit Weigth (g)"
           , glab = "Interstock"
           , ylimits = c(0, 600, 100)
           , 
           )

p2a

5.2.2 Fruit length

trait <- "long"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##  [1] index       block       stock       edge        long        resi       
##  [7] res_MAD     rawp.BHStud adjp        bholm       out_flag   
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: long
##             Df  Sum Sq Mean Sq F value   Pr(>F)   
## block        2   681.4  340.70  5.9312 0.003081 **
## stock        1    31.2   31.16  0.5424 0.462192   
## edge         3   270.1   90.02  1.5672 0.198107   
## stock:edge   3    39.5   13.15  0.2289 0.876184   
## Residuals  229 13154.4   57.44                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
1	JULIE	CHATO	107.6667	1.383746	229	104.9402	110.3932	a
3	CHULUCANAS	CHATO	106.3048	1.407606	229	103.5313	109.0783	a
2	IRWIN	CHATO	106.0667	1.383746	229	103.3402	108.7932	a
4	CHATO	CHATO	105.9667	1.383746	229	103.2402	108.6932	a
6	JULIE	CHULUCANAS	109.7333	1.383746	229	107.0068	112.4598	a
7	CHATO	CHULUCANAS	106.5333	1.383746	229	103.8068	109.2598	a
5	IRWIN	CHULUCANAS	106.4333	1.383746	229	103.7068	109.1598	a
8	CHULUCANAS	CHULUCANAS	106.2000	1.383746	229	103.4735	108.9265	a

p2b <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit length (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 120, 20)
           , 
           )

p2b

5.2.3 Fruit diameter

trait <- "diameter_average"

lmm <- paste({{trait}}, "~ 1 + (1|block) + stock*edge") %>% as.formula()

lmd <- paste({{trait}}, "~ block + stock*edge") %>% as.formula()

rmout <- fru %>% 
  remove_outliers(formula = lmm
                  , drop_na = T, plot_diag = T)

rmout$diagplot

rmout$outliers
##  [1] index            block            stock            edge            
##  [5] diameter_average resi             res_MAD          rawp.BHStud     
##  [9] adjp             bholm            out_flag        
## <0 rows> (o 0- extensión row.names)

model <- rmout$data$clean %>% 
  aov(formula = lmd, .)

anova(model)
## Analysis of Variance Table
## 
## Response: diameter_average
##             Df Sum Sq Mean Sq F value   Pr(>F)   
## block        2  248.1 124.033  5.0489 0.007149 **
## stock        1    1.2   1.178  0.0480 0.826864   
## edge         3    6.1   2.027  0.0825 0.969504   
## stock:edge   3  234.3  78.109  3.1796 0.024804 * 
## Residuals  229 5625.6  24.566                    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

mc <- emmeans(model, ~ edge|stock) %>%
  cld(Letters = letters, reversed = T) %>%
  mutate(across(.group, trimws)) %>% 
  rename(group = ".group")

mc %>% kable()

	edge	stock	emmean	SE	df	lower.CL	upper.CL	group
1	IRWIN	CHATO	87.63333	0.9049151	229	85.85031	89.41636	a
2	JULIE	CHATO	86.23333	0.9049151	229	84.45031	88.01636	a
4	CHULUCANAS	CHATO	86.07080	0.9205182	229	84.25703	87.88457	a
3	CHATO	CHATO	85.23333	0.9049151	229	83.45031	87.01636	a
7	CHATO	CHULUCANAS	87.70000	0.9049151	229	85.91698	89.48302	a
6	JULIE	CHULUCANAS	86.30000	0.9049151	229	84.51698	88.08302	a
8	CHULUCANAS	CHULUCANAS	86.08333	0.9049151	229	84.30031	87.86636	a
5	IRWIN	CHULUCANAS	84.53333	0.9049151	229	82.75031	86.31636	a

p2c <- mc %>% 
  plot_smr(x = "stock"
           , y = "emmean"
           , group = "edge"
           , sig = "group"
           , error = "SE"
           , color = T
           , xlab = "Rootstock"
           , ylab = "Fruit diameter (mm)"
           , glab = "Interstock"
           , ylimits = c(0, 100, 20)
           , 
           )

p2c

5.2.4 Table 2

Descriptive statistics of the variables that determine the fruit biometrics of mango.

sts <- Summarize(weigth  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1a <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit Weigth (g)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(long  ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1b <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit length (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

sts <- Summarize(diameter_average ~ stock*edge, data = fru, digits = 2, na.rm = TRUE)

tb1c <- sts%>% 
  merge(., mc) %>% 
  mutate(Variable = "Fruit diameter (mm)") %>% 
  dplyr::select(Variable, stock, edge, mean, sd, min, max, group) %>% 
  rename(Rootstock = stock,
         Interstock = edge,
         Sig = group)

tb1 <- bind_rows(tb1a, tb1b, tb1c)

tb1 %>% kable(align = 'c')

Variable	Rootstock	Interstock	mean	sd	min	max	Sig
Fruit Weigth (g)	CHATO	CHATO	452.53	79.92	305.0	639.0	a
Fruit Weigth (g)	CHATO	CHULUCANAS	462.34	69.82	340.0	620.0	a
Fruit Weigth (g)	CHATO	IRWIN	482.03	87.30	350.0	700.0	a
Fruit Weigth (g)	CHATO	JULIE	465.97	92.07	300.0	645.0	a
Fruit Weigth (g)	CHULUCANAS	CHATO	484.97	101.98	316.0	765.0	a
Fruit Weigth (g)	CHULUCANAS	CHULUCANAS	468.17	118.73	230.0	665.0	a
Fruit Weigth (g)	CHULUCANAS	IRWIN	447.23	70.09	310.0	605.0	a
Fruit Weigth (g)	CHULUCANAS	JULIE	484.70	93.46	367.0	717.0	a
Fruit length (mm)	CHATO	CHATO	105.97	6.78	93.0	120.0	a
Fruit length (mm)	CHATO	CHULUCANAS	106.28	6.80	94.0	119.0	a
Fruit length (mm)	CHATO	IRWIN	106.07	6.47	95.0	124.0	a
Fruit length (mm)	CHATO	JULIE	107.67	7.54	91.0	120.0	a
Fruit length (mm)	CHULUCANAS	CHATO	106.53	7.21	95.0	123.0	a
Fruit length (mm)	CHULUCANAS	CHULUCANAS	106.20	10.87	86.0	126.0	a
Fruit length (mm)	CHULUCANAS	IRWIN	106.43	7.28	92.0	122.0	a
Fruit length (mm)	CHULUCANAS	JULIE	109.73	8.03	98.0	129.0	a
Fruit diameter (mm)	CHATO	CHATO	85.23	4.72	76.0	97.5	a
Fruit diameter (mm)	CHATO	CHULUCANAS	86.09	3.61	78.5	93.5	a
Fruit diameter (mm)	CHATO	IRWIN	87.63	5.07	79.5	100.0	a
Fruit diameter (mm)	CHATO	JULIE	86.23	5.56	75.5	97.5	a
Fruit diameter (mm)	CHULUCANAS	CHATO	87.70	5.40	78.0	100.5	a
Fruit diameter (mm)	CHULUCANAS	CHULUCANAS	86.08	6.61	69.5	99.0	a
Fruit diameter (mm)	CHULUCANAS	IRWIN	84.53	4.03	77.5	92.5	a
Fruit diameter (mm)	CHULUCANAS	JULIE	86.30	4.68	79.5	97.0	a

tb1 %>%
  write_sheet(ss = gs, sheet = "tb1")

5.2.5 Multivariate

Principal Component Analysis (PCA) of fruit biometrics in the mango crop based on the use of rootstock-interstock combinations.

mv <- fru %>% 
  group_by(stock, edge) %>% 
  summarise(across(where(is.numeric), ~ mean(., na.rm = T))) %>%
  dplyr::select(!c(diameter_1, diameter_2, n_fruits)) %>%
  unite("treat", stock:edge, sep = "-") %>% 
   rename(Treat = treat
         , Weight = weigth
         , Length = long
         , Diameter = diameter_average)
  
pca <- mv %>% 
  PCA(scale.unit = T, quali.sup = 1, graph = F) 

# summary

summary(pca, nbelements = Inf, nb.dec = 2)
## 
## Call:
## PCA(X = ., scale.unit = T, quali.sup = 1, graph = F) 
## 
## 
## Eigenvalues
##                       Dim.1  Dim.2  Dim.3
## Variance               2.01   0.97   0.02
## % of var.             66.97  32.23   0.79
## Cumulative % of var.  66.97  99.21 100.00
## 
## Individuals
##                          Dist   Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3
## 1                     |  1.71 | -1.70 17.91  0.98 | -0.22  0.60  0.02 | -0.01
## 2                     |  0.68 | -0.56  1.99  0.69 | -0.37  1.76  0.29 |  0.09
## 3                     |  1.85 |  1.38 11.78  0.56 | -1.23 19.60  0.44 |  0.00
## 4                     |  0.70 |  0.09  0.05  0.02 |  0.61  4.83  0.76 |  0.33
## 5                     |  1.93 |  1.70 17.92  0.78 | -0.91 10.79  0.22 |  0.00
## 6                     |  0.57 | -0.29  0.51  0.25 | -0.44  2.46  0.58 | -0.23
## 7                     |  2.33 | -2.29 32.54  0.96 |  0.45  2.60  0.04 | -0.06
## 8                     |  2.69 |  1.67 17.30  0.38 |  2.11 57.36  0.61 | -0.12
##                         ctr  cos2  
## 1                      0.03  0.00 |
## 2                      4.74  0.02 |
## 3                      0.00  0.00 |
## 4                     57.67  0.22 |
## 5                      0.00  0.00 |
## 6                     28.15  0.16 |
## 7                      2.18  0.00 |
## 8                      7.23  0.00 |
## 
## Variables
##                         Dim.1   ctr  cos2   Dim.2   ctr  cos2   Dim.3   ctr
## Weight                |  0.99 49.14  0.99 | -0.03  0.07  0.00 | -0.11 50.79
## Length                |  0.46 10.55  0.21 |  0.89 81.29  0.79 |  0.04  8.16
## Diameter              |  0.90 40.32  0.81 | -0.42 18.64  0.18 |  0.10 41.05
##                        cos2  
## Weight                 0.01 |
## Length                 0.00 |
## Diameter               0.01 |
## 
## Supplementary categories
##                          Dist   Dim.1  cos2 v.test   Dim.2  cos2 v.test   Dim.3
## CHATO-CHATO           |  1.71 | -1.70  0.98  -1.20 | -0.22  0.02  -0.22 | -0.01
## CHATO-CHULUCANAS      |  0.68 | -0.56  0.69  -0.40 | -0.37  0.29  -0.38 |  0.09
## CHATO-IRWIN           |  1.85 |  1.38  0.56   0.97 | -1.23  0.44  -1.25 |  0.00
## CHATO-JULIE           |  0.70 |  0.09  0.02   0.07 |  0.61  0.76   0.62 |  0.33
## CHULUCANAS-CHATO      |  1.93 |  1.70  0.78   1.20 | -0.91  0.22  -0.93 |  0.00
## CHULUCANAS-CHULUCANAS |  0.57 | -0.29  0.25  -0.20 | -0.44  0.58  -0.44 | -0.23
## CHULUCANAS-IRWIN      |  2.33 | -2.29  0.96  -1.61 |  0.45  0.04   0.46 | -0.06
## CHULUCANAS-JULIE      |  2.69 |  1.67  0.38   1.18 |  2.11  0.61   2.14 | -0.12
##                        cos2 v.test  
## CHATO-CHATO            0.00  -0.05 |
## CHATO-CHULUCANAS       0.02   0.62 |
## CHATO-IRWIN            0.00  -0.02 |
## CHATO-JULIE            0.22   2.15 |
## CHULUCANAS-CHATO       0.00  -0.02 |
## CHULUCANAS-CHULUCANAS  0.16  -1.50 |
## CHULUCANAS-IRWIN       0.00  -0.42 |
## CHULUCANAS-JULIE       0.00  -0.76 |

f5a <- plot.PCA(x = pca, choix = "var"
                , cex=0.8
                )

f5b <- plot.PCA(x = pca, choix = "ind"
                , habillage = 1
                , invisible = c("ind")
                , cex=0.8
                ) 

5.2.6 Figure 5

Principal Component Analysis (PCA). Results of the contributions and correlation of the variables in the Principal Component Analysis (PCA).

var <- get_pca_var(pca)

pt1 <- fviz_eig(pca, 
                addlabels=TRUE,
                hjust = 0.05,
                barfill="white",
                barcolor ="darkblue",
                linecolor ="white") + 
  ylim(0, 80) + 
  labs(
    title = "PCA - percentage of explained variances",
    y = "Variance (%)") +
  theme_minimal()

pt2 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 1, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 60) + 
  labs(title = "Dim 1 - variables contribution") 

pt3 <- fviz_contrib(pca,
                     choice = "var", 
                     axes = 2, 
                     top = 10,
                     fill="white",
                     color ="darkblue",
                     sort.val = "desc") +
  ylim(0, 100) + 
  labs(title = "Dim 2 - variables contribution") 

png("corrplot_output.png", width = 800, height = 800, res = 150)  
corrplot(var$cor, 
         method = "number",
         tl.col = "black", 
         tl.srt = 45)
dev.off()
## png 
##   2

img_corrplot <- ggdraw() + draw_image("corrplot_output.png")

pt4 <- img_corrplot

# Construcción del grid

fila1 <- plot_grid(f5a, f5b, ncol = 2, labels = "auto", rel_widths = c(1, 1.2))

columna_c <- plot_grid(pt1, pt2, pt3, ncol = 1, labels = "c")

fila2 <- plot_grid(columna_c, pt4, ncol = 2, labels = c("", "d"), rel_widths = c(1, 1))

grid_final <- plot_grid(fila1, fila2, ncol = 1, rel_heights = c(1, 1.2))

grid_final

ggsave2(plot = grid_final, "submission/Figure_5.jpg", height = 30, width = 28, units = "cm")

ggsave2(plot = grid_final, "submission/Figure_5.eps", height = 30, width = 28, units = "cm")

knitr::include_graphics("submission/Figure_5.jpg")